AI Engineering Podcast

SummaryEvery business develops their own specific workflows to address their internal organizational needs. Not all of them are properly documented, or even visible. Workflow automation tools have tried to reduce the manual burden involved, but they are rigid and require substantial investment of time to discover and develop the routines. Boaz Hecht co-founded 8Flow to iteratively discover and automate pieces of workflows, bringing visibility and collaboration to the internal organizational processes that keep the business running.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Boaz Hecht about using AI to automate customer support at 8FlowInterviewIntroductionHow did you get involved in machine learning?Can you describe what 8Flow is and the story behind it?How does 8Flow compare to RPA tools that companies are using today? What are the opportunities for augmenting or integrating with RPA frameworks?What are the key selling points for the solution that you are building? (does AI sell? Or is it about the realized savings?)What are the sources of signal that you are relying on to build model features?Given the heterogeneity in tools and processes across customers, what are the common focal points that let you address the widest possible range of functionality?Can you describe how 8Flow is implemented? How have the design and goals evolved since you first started working on it?What are the model categories that are most relevant for process automation in your product?How have you approached the design and implementation of your MLOps workflow? (model training, deployment, monitoring, versioning, etc.)What are the open questions around product focus and system design that you are still grappling with?Given the relative recency of ML/AI as a profession and the massive growth in attention and activity, how are you addressing the challenge of obtaining and maximizing human talent?What are the most interesting, innovative, or unexpected ways that you have seen 8Flow used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on 8Flow?When is 8Flow the wrong choice?What do you have planned for the future of 8Flow?Contact InfoLinkedInPersonal WebsiteParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.Links8FlowRobotic Process AutomationThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0Support The Machine Learning Podcast

SummaryMachine learning and AI applications hold the promise of drastically impacting every aspect of modern life. With that potential for profound change comes a responsibility for the creators of the technology to account for the ramifications of their work. In this episode Nicholas Cifuentes-Goodbody guides us through the minefields of social, technical, and ethical considerations that are necessary to ensure that this next generation of technical and economic systems are equitable and beneficial for the people that they impact.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Nicholas Cifuentes-Goodbody about the different elements of the machine learning workflow where ethics need to be consideredInterviewIntroductionHow did you get involved in machine learning?To start with, who is responsible for addressing the ethical concerns around AI?What are the different ways that AI can have positive or negative outcomes from an ethical perspective? What is the role of practitioners/individual contributors in the identification and evaluation of ethical impacts of their work?What are some utilities that are helpful in identifying and addressing bias in training data?How can practitioners address challenges of equity and accessibility in the delivery of AI products?What are some of the options for reducing the energy consumption for training and serving AI?What are the most interesting, innovative, or unexpected ways that you have seen ML teams incorporate ethics into their work?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ethical implications of ML?What are some of the resources that you recommend for people who want to invest in their knowledge and application of ethics in the realm of ML?Contact InfoWorldQuant University's Applied Data Science LabLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksUNESCO Recommendation on the Ethics of Artificial IntelligenceEuropean Union AI ActHow machine learning helps advance access to human rights informationDisinformation, Team JorgeChina, AI, and Human RightsHow China Is Using A.I. to Profile a MinorityWeapons of Math DestructionFairlearnAI Fairness 360Allen Institute for AI NYTAllen Institute for AITransformersAI4ALLWorldQuant UniversityHow to Make Generative AI GreenerMachine Learning Emissions CalculatorPracticing Trustworthy Machine LearningEnergy and Policy Considerations for Deep LearningNatural Language ProcessingTrolley ProblemProtected Classesfairlearn (scikit-learn)BERT ModelThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryBuilding machine learning systems and other intelligent applications are a complex undertaking. This often requires retrieving data from a warehouse engine, adding an extra barrier to every workflow. The RelationalAI engine was built as a co-processor for your data warehouse that adds a greater degree of flexibility in the representation and analysis of the underlying information, simplifying the work involved. In this episode CEO Molham Aref explains how RelationalAI is designed, the capabilities that it adds to your data clouds, and how you can start using it to build more sophisticated applications on your data.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Molham Aref about RelationalAI and the principles behind it for powering intelligent applicationsInterviewIntroductionHow did you get involved in machine learning?Can you describe what RelationalAI is and the story behind it? On your site you call your product an "AI Co-processor". Can you explain what you mean by that phrase?What are the primary use cases that you address with the RelationalAI product? What are the types of solutions that teams might build to address those problems in the absence of something like the RelationalAI engine?Can you describe the system design of RelationalAI? How have the design and goals of the platform changed since you first started working on it?For someone who is using RelationalAI to address a business need, what does the onboarding and implementation workflow look like?What is your design philosophy for identifying the balance between automating the implementation of certain categories of application (e.g. NER) vs. providing building blocks and letting teams assemble them on their own?What are the data modeling paradigms that teams should be aware of to make the best use of the RKGS platform and Rel language?What are the aspects of customer education that you find yourself spending the most time on?What are some of the most under-utilized or misunderstood capabilities of the RelationalAI platform that you think deserve more attention?What are the most interesting, innovative, or unexpected ways that you have seen the RelationalAI product used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on RelationalAI?When is RelationalAI the wrong choice?What do you have planned for the future of RelationalAI?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksRelationalAISnowflakeAI WinterBigQueryGradient DescentB-TreeNavigational DatabaseHadoopTeradataWorst Case Optimal JoinSemantic Query OptimizationRelational AlgebraHyperGraphLinear AlgebraVector DatabasePathwayData Engineering Podcast EpisodePineconeData Engineering Podcast EpisodeThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryMachine learning and generative AI systems have produced truly impressive capabilities. Unfortunately, many of these applications are not designed with the privacy of end-users in mind. TripleBlind is a platform focused on embedding privacy preserving techniques in the machine learning process to produce more user-friendly AI products. In this episode Gharib Gharibi explains how the current generation of applications can be susceptible to leaking user data and how to counteract those trends.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Gharib Gharibi about the challenges of bias and data privacy in generative AI modelsInterviewIntroductionHow did you get involved in machine learning?Generative AI has been gaining a lot of attention and speculation about its impact. What are some of the risks that these capabilities pose? What are the main contributing factors to their existing shortcomings?What are some of the subtle ways that bias in the source data can manifest?In addition to inaccurate results, there is also a question of how user interactions might be re-purposed and potential impacts on data and personal privacy. What are the main sources of risk?With the massive attention that generative AI has created and the perspectives that are being shaped by it, how do you see that impacting the general perception of other implementations of AI/ML? How can ML practitioners improve and convey the trustworthiness of their models to end users?What are the risks for the industry if generative models fall out of favor with the public?How does your work at Tripleblind help to encourage a conscientious approach to AI?What are the most interesting, innovative, or unexpected ways that you have seen data privacy addressed in AI applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on privacy in AI?When is TripleBlind the wrong choice?What do you have planned for the future of TripleBlind?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksTripleBlindImageNet Geoffrey Hinton PaperBERT language modelGenerative AIGPT == Generative Pre-trained TransformerHIPAA Safe Harbor RulesFederated LearningDifferential PrivacyHomomorphic EncryptionThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummarySoftware development involves an interesting balance of creativity and repetition of patterns. Generative AI has accelerated the ability of developer tools to provide useful suggestions that speed up the work of engineers. Tabnine is one of the main platforms offering an AI powered assistant for software engineers. In this episode Eran Yahav shares the journey that he has taken in building this product and the ways that it enhances the ability of humans to get their work done, and when the humans have to adapt to the tool.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Eran Yahav about building an AI powered developer assistant at TabnineInterviewIntroductionHow did you get involved in machine learning?Can you describe what Tabnine is and the story behind it?What are the individual and organizational motivations for using AI to generate code? What are the real-world limitations of generative AI for creating software? (e.g. size/complexity of the outputs, naming conventions, etc.)What are the elements of skepticism/oversight that developers need to exercise while using a system like Tabnine?What are some of the primary ways that developers interact with Tabnine during their development workflow? Are there any particular styles of software for which an AI is more appropriate/capable? (e.g. webapps vs. data pipelines vs. exploratory analysis, etc.)For natural languages there is a strong bias toward English in the current generation of LLMs. How does that translate into computer languages? (e.g. Python, Java, C++, etc.)Can you describe the structure and implementation of Tabnine? Do you rely primarily on a single core model, or do you have multiple models with subspecialization?How have the design and goals of the product changed since you first started working on it?What are the biggest challenges in building a custom LLM for code? What are the opportunities for specialization of the model architecture given the highly structured nature of the problem domain?For users of Tabnine, how do you assess/monitor the accuracy of recommendations? What are the feedback and reinforcement mechanisms for the model(s)?What are the most interesting, innovative, or unexpected ways that you have seen Tabnine's LLM powered coding assistant used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on AI assisted development at Tabnine?When is an AI developer assistant the wrong choice?What do you have planned for the future of Tabnine?Contact InfoLinkedInWebsiteParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksTabNineTechnion UniversityProgram SynthesisContext StuffingElixirDependency InjectionCOBOLVerilogMidJourneyThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummarySoftware systems power much of the modern world. For applications that impact the safety and well-being of people there is an extra set of precautions that need to be addressed before deploying to production. If machine learning and AI are part of that application then there is a greater need to validate the proper functionality of the models. In this episode Erez Kaminski shares the work that he is doing at Ketryx to make that validation easier to implement and incorporate into the ongoing maintenance of software and machine learning products.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Erez Kaminski about using machine learning in safety critical and highly regulated medical applicationsInterviewIntroductionHow did you get involved in machine learning?Can you start by describing some of the regulatory burdens placed on ML teams who are building solutions for medical applications? How do these requirements impact the development and validation processes of model design and development?What are some examples of the procedural and record-keeping aspects of the machine learning workflow that are required for FDA compliance? What are the opportunities for automating pieces of that overhead?Can you describe what you are doing at Ketryx to streamline the development/training/deployment of ML/AI applications for medical use cases? What are the ideas/assumptions that you had at the start of Ketryx that have been challenged/updated as you work with customers?What are the most interesting, innovative, or unexpected ways that you have seen ML used in medical applications?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Ketryx?When is Ketryx the wrong choice?What do you have planned for the future of Ketryx?Contact InfoEmailLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workers.LinksKetryxWolfram AlphaMathematicaTensorflowSBOM == Software Bill Of MaterialsAir-gapped SystemsAlexNetShapley ValuesSHAPPodcast.__init__ EpisodeBayesian StatisticsCausal ModelingProphetFDA Principles Of Software ValidationThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryLarge language models have gained a substantial amount of attention in the area of AI and machine learning. While they are impressive, there are many applications where they are not the best option. In this episode Piero Molino explains how declarative ML approaches allow you to make the best use of the available tools across use cases and data formats.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Piero Molino about the application of declarative ML in a world being dominated by large language modelsInterviewIntroductionHow did you get involved in machine learning?Can you start by summarizing your perspective on the effect that LLMs are having on the AI/ML industry? In a world where LLMs are being applied to a growing variety of use cases, what are the capabilities that they still lack?How does declarative ML help to address those shortcomings?The majority of current hype is about commercial models (e.g. GPT-4). Can you summarize the current state of the ecosystem for open source LLMs? For teams who are investing in ML/AI capabilities, what are the sources of platform risk for LLMs?What are the comparative benefits of using a declarative ML approach?What are the most interesting, innovative, or unexpected ways that you have seen LLMs used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on declarative ML in the age of LLMs?When is an LLM the wrong choice?What do you have planned for the future of declarative ML and Predibase?Contact InfoLinkedInWebsiteClosing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?LinksPredibasePodcast EpisodeLudwigPodcast.__init__ EpisodeRecommender SystemsInformation RetrievalVector DatabaseTransformer ModelBERTContext WindowsLLAMAThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryArtificial Intelligence is experiencing a renaissance in the wake of breakthrough natural language models. With new businesses sprouting up to address the various needs of ML and AI teams across the industry, it is a constant challenge to stay informed. Matt Turck has been compiling a report on the state of ML, AI, and Data for his work at FirstMark Capital. In this episode he shares his findings on the ML and AI landscape and the interesting trends that are developing.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.As more people start using AI for projects, two things are clear: It’s a rapidly advancing field, but it’s tough to navigate. How can you get the best results for your use case? Instead of being subjected to a bunch of buzzword bingo, hear directly from pioneers in the developer and data science space on how they use graph tech to build AI-powered apps. . Attend the dev and ML talks at NODES 2023, a free online conference on October 26 featuring some of the brightest minds in tech. Check out the agenda and register today at Neo4j.com/NODES.Your host is Tobias Macey and today I'm interviewing Matt Turck about his work on the MAD (ML, AI, and Data) landscape and the insights he has gained on the ML ecosystemInterviewIntroductionHow did you get involved in machine learning?Can you describe what the MAD landscape project is and the story behind it?What are the major changes in the ML ecosystem that you have seen since you first started compiling the landscape? How have the developments in consumer-grade AI in recent years changed the business opportunities for ML/AI?What are the coarse divisions that you see as the boundaries that define the different categories for ML/AI in the landscape?For ML infrastructure products/companies, what are the biggest challenges that they face in engineering and customer acquisition?What are some of the challenges in building momentum for startups in AI (existing moats around data access, talent acquisition, etc.)? For products/companies that have ML/AI as their core offering, what are some strategies that they use to compete with "big tech" companies that already have a large corpus of data?What do you see as the societal vs. business importance of open source models as AI becomes more integrated into consumer facing products?What are the most interesting, innovative, or unexpected ways that you have seen ML/AI used in business and social contexts?What are the most interesting, unexpected, or challenging lessons that you have learned while working on the ML/AI elements of the MAD landscape?When is ML/AI the wrong choice for businesses?What are the areas of ML/AI that you are paying closest attention to in your own work?Contact InfoWebsite@mattturck on TwitterParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksMAD LandscapeData Engineering Podcast EpisodeFirst Mark CapitalBayesian TechniquesHadoopChatGPTAutoGPTDataikuGenerative AIDatabricksMLOpsOpenAIAnthropicDeepMindBloombergGPTHuggingFaceJexi Movie"Her" MovieSynthesiaThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryA core challenge of machine learning systems is getting access to quality data. This often means centralizing information in a single system, but that is impractical in highly regulated industries, such as healthchare. To address this hurdle Rhino Health is building a platform for federated learning on health data, so that everyone can maintain data privacy while benefiting from AI capabilities. In this episode Ittai Dayan explains the barriers to ML in healthcare and how they have designed the Rhino platform to overcome them.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Ittai Dayan about using federated learning at Rhino Health to bring AI capabilities to the tightly regulated healthcare industryInterviewIntroductionHow did you get involved in machine learning?Can you describe what Rhino Health is and the story behind it?What is federated learning and what are the trade-offs that it introduces? What are the benefits to healthcare and pharmalogical organizations from using federated learning?What are some of the challenges that you face in validating that patient data is properly de-identified in the federated models?Can you describe what the Rhino Health platform offers and how it is implemented? How have the design and goals of the system changed since you started working on it?What are the technological capabilities that are needed for an organization to be able to start using Rhino Health to gain insights into their patient and clinical data? How have you approached the design of your product to reduce the effort to onboard new customers and solutions?What are some examples of the types of automation that you are able to provide to your customers? (e.g. medical diagnosis, radiology review, health outcome predictions, etc.)What are the ethical and regulatory challenges that you have had to address in the development of your platform?What are the most interesting, innovative, or unexpected ways that you have seen Rhino Health used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Rhino Health?When is Rhino Health the wrong choice?What do you have planned for the future of Rhino Health?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksRhino HealthFederated LearningNvidia ClaraNvidia DGXMelloddyFlair NLPThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummarySatellite imagery has given us a new perspective on our world, but it is limited by the field of view for the cameras. Synthetic Aperture Radar (SAR) allows for collecting images through clouds and in the dark, giving us a more consistent means of collecting data. In order to identify interesting details in such a vast amount of data it is necessary to use the power of machine learning. ICEYE has a fleet of satellites continuously collecting information about our planet. In this episode Tapio Friberg shares how they are applying ML to that data set to provide useful insights about fires, floods, and other terrestrial phenomena.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Tapio Friberg about building machine learning applications on top of SAR (Synthetic Aperture Radar) data to generate insights about our planetInterviewIntroductionHow did you get involved in machine learning?Can you describe what ICEYE is and the story behind it?What are some of the applications of ML at ICEYE?What are some of the ways that SAR data poses a unique challenge to ML applications?What are some of the elements of the ML workflow that you are able to use "off the shelf" and where are the areas that you have had to build custom solutions?Can you share the structure of your engineering team and the role that the ML function plays in the larger organization?What does the end-to-end workflow for your ML model development and deployment look like? What are the operational requirements for your models? (e.g. batch execution, real-time, interactive inference, etc.)In the model definitions, what are the elements of the source domain that create the largest challenges? (e.g. noise from backscatter, variance in resolution, etc.)Once you have an output from an ML model how do you manage mapping between data domains to reflect insights from SAR sources onto a human understandable representation?Given that SAR data and earth imaging is still a very niche domain, how does that influence your ability to hire for open positions and the ways that you think about your contributions to the overall ML ecosystem?How can your work on using SAR as a representation of physical attributes help to improve capabilities in e.g. LIDAR, computer vision, etc.?What are the most interesting, innovative, or unexpected ways that you have seen ICEYE and SAR data used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML for SAR data?What do you have planned for the future of ML applications at ICEYE?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksICEYESAR == Synthetic Aperture RadarTransfer LearningThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryThe focus of machine learning projects has long been the model that is built in the process. As AI powered applications grow in popularity and power, the model is just the beginning. In this episode Josh Tobin shares his experience from his time as a machine learning researcher up to his current work as a founder at Gantry, and the shift in focus from model development to machine learning systems.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Josh Tobin about the state of industry best practices for designing and building ML modelsInterviewIntroductionHow did you get involved in machine learning?Can you start by describing what a "traditional" process for building a model looks like? What are the forces that shaped those "best practices"?What are some of the practices that are still necessary/useful and what is becoming outdated? What are the changes in the ecosystem (tooling, research, communal knowledge, etc.) that are forcing teams to reconsider how they think about modeling?What are the most critical practices/capabilities for teams who are building services powered by ML/AI? What systems do they need to support them in those efforts?Can you describe what you are building at Gantry and how it aids in the process of developing/deploying/maintaining models with "modern" workflows?What are the most challenging aspects of building a platform that supports ML teams in their workflows?What are the most interesting, innovative, or unexpected ways that you have seen teams approach model development/validation?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Gantry?When is Gantry the wrong choice?What are some of the resources that you find most helpful to stay apprised of how modeling and ML practices are evolving?Contact InfoLinkedInWebsiteParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksGantryFull Stack Deep LearningOpenAIKaggleNeurIPS == Neural Information Processing Systems ConferenceCaffeTheanoDeep LearningRegression Modelscikit-learnLarge Language ModelFoundation ModelsCohereFederated LearningFeature StoredbtThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryMachine learning models have predominantly been built and updated in a batch modality. While this is operationally simpler, it doesn't always provide the best experience or capabilities for end users of the model. Tecton has been investing in the infrastructure and workflows that enable building and updating ML models with real-time data to allow you to react to real-world events as they happen. In this episode CTO Kevin Stumpf explores they benefits of real-time machine learning and the systems that are necessary to support the development and maintenance of those models.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Kevin Stumpf about the challenges and promise of real-time ML applicationsInterviewIntroductionHow did you get involved in machine learning?Can you describe what real-time ML is and some examples of where it might be applied?What are the operational and organizational requirements for being able to adopt real-time approaches for ML projects?What are some of the ways that real-time requirements influence the scale/scope/architecture of an ML model?What are some of the failure modes for real-time vs analytical or operational ML?Given the low latency between source/input data being generated or received and a prediction being generated, how does that influence susceptibility to e.g. data drift? Data quality and accuracy also become more critical. What are some of the validation strategies that teams need to consider as they move to real-time?What are the most interesting, innovative, or unexpected ways that you have seen real-time ML applied?What are the most interesting, unexpected, or challenging lessons that you have learned while working on real-time ML systems?When is real-time the wrong choice for ML?What do you have planned for the future of real-time support for ML in Tecton?Contact InfoLinkedIn@kevinmstumpf on TwitterParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksTectonPodcast EpisodeData Engineering Podcast EpisodeUber MichelangeloReinforcement LearningOnline LearningRandom ForestChatGPTXGBoostLinear RegressionTrain-Serve SkewFlinkData Engineering Podcast EpisodeThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryShopify uses machine learning to power multiple features in their platform. In order to reduce the amount of effort required to develop and deploy models they have invested in building an opinionated platform for their engineers. They have gone through multiple iterations of the platform and their most recent version is called Merlin. In this episode Isaac Vidas shares the use cases that they are optimizing for, how it integrates into the rest of their data platform, and how they have designed it to let machine learning engineers experiment freely and safely.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Isaac Vidas about his work on the ML platform used by ShopifyInterviewIntroductionHow did you get involved in machine learning?Can you describe what Shopify is and some of the ways that you are using ML at Shopify? What are the challenges that you have encountered as an organization in applying ML to your business needs?Can you describe how you have designed your current technical platform for supporting ML workloads? Who are the target personas for this platform?What does the workflow look like for a given data scientist/ML engineer/etc.?What are the capabilities that you are trying to optimize for in your current platform? What are some of the previous iterations of ML infrastructure and process that you have built?What are the most useful lessons that you gathered from those previous experiences that informed your current approach?How have the capabilities of the Merlin platform influenced the ways that ML is viewed and applied across Shopify?What are the most interesting, innovative, or unexpected ways that you have seen Merlin used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Merlin?When is Merlin the wrong choice?What do you have planned for the future of Merlin?Contact Info@kazuaros on TwitterLinkedInkazuar on GitHubParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksShopifyShopify MerlinVertex AIscikit-learnXGBoostRayPodcast.__init__ EpisodePySparkGPT-3ChatGPTGoogle AIPyTorchPodcast.__init__ EpisodeDaskModinPodcast.__init__ EpisodeFlinkData Engineering Podcast EpisodeFeast Feature StoreKubernetesThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryAll data systems are subject to the "garbage in, garbage out" problem. For machine learning applications bad data can lead to unreliable models and unpredictable results. Anomalo is a product designed to alert on bad data by applying machine learning models to various storage and processing systems. In this episode Jeremy Stanley discusses the various challenges that are involved in building useful and reliable machine learning models with unreliable data and the interesting problems that they are solving in the process.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Your host is Tobias Macey and today I'm interviewing Jeremy Stanley about his work at Anomalo, applying ML to the problem of data quality monitoringInterviewIntroductionHow did you get involved in machine learning?Can you describe what Anomalo is and the story behind it?What are some of the ML approaches that you are using to address challenges with data quality/observability?What are some of the difficulties posed by your application of ML technologies on data sets that you don't control? How does the scale and quality of data that you are working with influence/constrain the algorithmic approaches that you are using to build and train your models?How have you implemented the infrastructure and workflows that you are using to support your ML applications?What are some of the ways that you are addressing data quality challenges in your own platform? What are the opportunities that you have for dogfooding your product?What are the most interesting, innovative, or unexpected ways that you have seen Anomalo used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anomalo?When is Anomalo the wrong choice?What do you have planned for the future of Anomalo?Contact Info@jeremystan on TwitterLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksAnomaloData Engineering Podcast EpisodePartial Differential EquationsNeural NetworkNeural Networks For Pattern Recognition by Christopher M. Bishop (affiliate link)Gradient Boosted Decision TreesShapley ValuesSentrydbtAltairThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryBuilding a machine learning model one time can be done in an ad-hoc manner, but if you ever want to update it and serve it in production you need a way of repeating a complex sequence of operations. Dagster is an orchestration engine that understands the data that it is manipulating so that you can move beyond coarse task-based representations of your dependencies. In this episode Sandy Ryza explains how his background in machine learning has informed his work on the Dagster project and the foundational principles that it is built on to allow for collaboration across data engineering and machine learning concerns.InterviewIntroductionHow did you get involved in machine learning?Can you start by sharing a definition of "orchestration" in the context of machine learning projects?What is your assessment of the state of the orchestration ecosystem as it pertains to ML?modeling cycles and managing experiment iterations in the execution graphhow to balance flexibility with repeatability What are the most interesting, innovative, or unexpected ways that you have seen orchestration implemented/applied for machine learning?What are the most interesting, unexpected, or challenging lessons that you have learned while working on orchestration of ML workflows?When is Dagster the wrong choice?What do you have planned for the future of ML support in Dagster?Contact InfoLinkedIn@s_ryz on Twittersryza on GitHubParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don't forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you've learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksDagsterData Engineering Podcast EpisodeClouderaHadoopApache SparkPeter NorvigJosh WillsREPL == Read Eval Print LoopRStudioMemoizationMLFlowKedroData Engineering Podcast EpisodeMetaflowPodcast.__init__ EpisodeKubeflowdbtData Engineering Podcast EpisodeAirbyteData Engineering Podcast EpisodeThe intro and outro music is from Hitman's Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryMachine learning is a data-hungry approach to problem solving. Unfortunately, there are a number of problems that would benefit from the automation provided by artificial intelligence capabilities that don’t come with troves of data to build from. Christopher Nguyen and his team at Aitomatic are working to address the "cold start" problem for ML by letting humans generate models by sharing their expertise through natural language. In this episode he explains how that works, the various ways that we can start to layer machine learning capabilities on top of each other, as well as the risks involved in doing so without incorporating lessons learned in the growth of the software industry.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Your host is Tobias Macey and today I’m interviewing Christopher Nguyen about how to address the cold start problem for ML/AI projectsInterviewIntroductionHow did you get involved in machine learning?Can you describe what the "cold start" or "small data" problem is and its impact on an organization’s ability to invest in machine learning?What are some examples of use cases where ML is a viable solution but there is a corresponding lack of usable data?How does the model design influence the data requirements to build it? (e.g. statistical model vs. deep learning, etc.)What are the available options for addressing a lack of data for ML? What are the characteristics of a given data set that make it suitable for ML use cases?Can you describe what you are building at Aitomatic and how it helps to address the cold start problem? How have the design and goals of the product changed since you first started working on it?What are some of the education challenges that you face when working with organizations to help them understand how to think about ML/AI investment and practical limitations? What are the most interesting, innovative, or unexpected ways that you have seen Aitomatic/H1st used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Aitomatic/H1st?When is a human/knowledge driven approach to ML development the wrong choice?What do you have planned for the future of Aitomatic?Contact InfoLinkedIn@pentagoniac on TwitterGoogle ScholarParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksAitomaticHuman First AIKnowledge First World SymposiumAtari 800Cold start problemScale AISnorkel AIPodcast EpisodeAnomaly DetectionExpert SystemsICML == International Conference on Machine LearningNIST == National Institute of Standards and TechnologyMulti-modal ModelSVM == Support Vector MachineTensorflowPytorchPodcast.__init__ EpisodeOSS CapitalDALL-EThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryData is one of the core ingredients for machine learning, but the format in which it is understandable to humans is not a useful representation for models. Embedding vectors are a way to structure data in a way that is native to how models interpret and manipulate information. In this episode Frank Liu shares how the Towhee library simplifies the work of translating your unstructured data assets (e.g. images, audio, video, etc.) into embeddings that you can use efficiently for machine learning, and how it fits into your workflow for model development.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!Your host is Tobias Macey and today I’m interviewing Frank Liu about how to use vector embeddings in your ML projects and how Towhee can reduce the effort involvedInterviewIntroductionHow did you get involved in machine learning?Can you describe what Towhee is and the story behind it?What is the problem that Towhee is aimed at solving?What are the elements of generating vector embeddings that pose the greatest challenge or require the most effort?Once you have an embedding, what are some of the ways that it might be used in a machine learning project? Are there any design considerations that need to be addressed in the form that an embedding takes and how it impacts the resultant model that relies on it? (whether for training or inference)Can you describe how the Towhee framework is implemented? What are some of the interesting engineering challenges that needed to be addressed?How have the design/goals/scope of the project shifted since it began?What is the workflow for someone using Towhee in the context of an ML project?What are some of the types optimizations that you have incorporated into Towhee? What are some of the scaling considerations that users need to be aware of as they increase the volume or complexity of data that they are processing?What are some of the ways that using Towhee impacts the way a data scientist or ML engineer approach the design development of their model code?What are the interfaces available for integrating with and extending Towhee?What are the most interesting, innovative, or unexpected ways that you have seen Towhee used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Towhee?When is Towhee the wrong choice?What do you have planned for the future of Towhee?Contact InfoLinkedInfzliu on GitHubWebsite@frankzliu on TwitterParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksTowheeZillizMilvusData Engineering Podcast EpisodeComputer VisionTensorAutoencoderLatent SpaceDiffusion ModelHSL == Hue, Saturation, LightnessWeights and BiasesThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryBecause machine learning models are constantly interacting with inputs from the real world they are subject to a wide variety of failures. The most commonly discussed error condition is concept drift, but there are numerous other ways that things can go wrong. In this episode Wojtek Kuberski explains how NannyML is designed to compare the predicted performance of your model against its actual behavior to identify silent failures and provide context to allow you to determine whether and how urgently to address them.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!Your host is Tobias Macey and today I’m interviewing Wojtek Kuberski about NannyML and the work involved in post-deployment data scienceInterviewIntroductionHow did you get involved in machine learning?Can you describe what NannyML is and the story behind it?What is "post-deployment data science"? How does it differ from the metrics/monitoring approach to managing the model lifecycle?Who is typically responsible for this work? How does NannyML augment their skills?What are some of your experiences with model failure that motivated you to spend your time and focus on this problem?What are the main contributing factors to alert fatigue for ML systems?What are some of the ways that a model can fail silently? How does NannyML detect those conditions?What are the remediation actions that might be necessary once an issue is detected in a model?Can you describe how NannyML is implemented? What are some of the technical and UX design problems that you have had to address?What are some of the ideas/assumptions that you have had to re-evaluate in the process of building NannyML?What additional capabilities are necessary for supporting less structured data?Can you describe what is involved in setting up NannyML and how it fits into an ML engineer’s workflow? Once a model is deployed, what additional outputs/data can/should be collected to improve the utility of NannyML and feed into analysis of the real-world operation?What are the most interesting, innovative, or unexpected ways that you have seen NannyML used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on NannyML?When is NannyML the wrong choice?What do you have planned for the future of NannyML?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksNannyMLF1 ScoreROC CurveConcept DriftA/B TestingJupyter NotebookVector EmbeddingAirflowEDA == Exploratory Data AnalysisInspired book (affiliate link)ZenMLThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryUsing machine learning in production requires a sophisticated set of cooperating technologies. A majority of resources that are available for understanding how to design and operate these platforms are focused on either simple examples that don’t scale, or over-engineered technologies designed for the massive scale of big tech companies. In this episode Jacopo Tagliabue shares his vision for "ML at reasonable scale" and how you can adopt these patterns for building your own platforms.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.Your host is Tobias Macey and today I’m interviewing Jacopo Tagliabue about building "reasonable scale" ML systemsInterviewIntroductionHow did you get involved in machine learning?How would you describe the current state of the ecosystem for ML practitioners? (e.g. tool selection, availability of information/tutorials, etc.) What are some of the notable changes that you have seen over the past 2 – 5 years?How have the evolutions in the data engineering space been reflected in/influenced the way that ML is being done?What are the challenges/points of friction that ML practitioners have to contend with when trying to get a model into production that isn’t just a toy?You wrote a set of tutorials and accompanying code about performing ML at "reasonable scale". What are you aiming to represent with that phrasing? There is a paradox of choice for any newcomer to ML. What are some of the key capabilities that practitioners should use in their decision rubric when designing a "reasonable scale" system?What are some of the common bottlenecks that crop up when moving from an initial test implementation to a scalable deployment that is serving customer traffic?How much of an impact does the type of ML problem being addressed have on the deployment and scalability elements of the system design? (e.g. NLP vs. computer vision vs. recommender system, etc.)What are some of the misleading pieces of advice that you have seen from "big tech" tutorials about how to do ML that are unnecessary when running at smaller scales?You also spend some time discussing the benefits of a "NoOps" approach to ML deployment. At what point do operations/infrastructure engineers need to get involved? What are the operational aspects of ML applications that infrastructure engineers working in product teams might be unprepared for?What are the most interesting, innovative, or unexpected system designs that you have seen for moderate scale MLOps?What are the most interesting, unexpected, or challenging lessons that you have learned while working on ML system design and implementation?What are the aspects of ML systems design that you are paying attention to in the current ecosystem?What advice do you have for additional references or research that ML practitioners would benefit from when designing their own production systems?Contact Infojacopotagliabue on GitHubWebsiteLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksThe Post-Modern Stack: ML At Reasonable ScaleCoveoNLP == Natural Language ProcessingRecListPart of speech taggingMarkov ModelYDNABB (You Don’t Need A Bigger Boat)dbtData Engineering Podcast EpisodeSeldonMetaflowPodcast.__init__ EpisodeSnowflakeInformation RetrievalModern Data StackSQLiteSpark SQLAWS AthenaKerasPyTorchLuigiAirflowFlaskAWS FargateAWS SagemakerRecommendations At Reasonable ScalePineconeData Engineering Podcast EpisodeRedisKNN == K-Nearest NeighborsPinterest Engineering BlogMaterializeOpenAIThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryThe increasing sophistication of machine learning has enabled dramatic transformations of businesses and introduced new product categories. At Assembly AI they are offering advanced speech recognition and natural language models as an API service. In this episode founder Dylan Fox discusses the unique challenges of building a business with machine learning as the core product.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Your host is Tobias Macey and today I’m interviewing Dylan Fox about building and growing a business with ML as its core offeringInterviewIntroductionHow did you get involved in machine learning?Can you describe what Assembly is and the story behind it? For anyone who isn’t familiar with your platform, can you describe the role that ML/AI plays in your product?What was your process for going from idea to prototype for an AI powered business? Can you offer parallels between your own experience and that of your peers who are building businesses oriented more toward pure software applications?How are you structuring your teams?On the path to your current scale and capabilities how have you managed scoping of your model capabilities and operational scale to avoid getting bogged down or burnt out?How do you think about scoping of model functionality to balance composability and system complexity?What is your process for identifying and understanding which problems are suited to ML and when to rely on pure software?You are constantly iterating on model performance and introducing new capabilities. How do you manage prototyping and experimentation cycles? What are the metrics that you track to identify whether and when to move from an experimental to an operational state with a model?What is your process for understanding what’s possible and what can feasibly operate at scale?Can you describe your overall operational patterns delivery process for ML?What are some of the most useful investments in tooling that you have made to manage development experience for your teams?Once you have a model in operation, how do you manage performance tuning? (from both a model and an operational scalability perspective)What are the most interesting, innovative, or unexpected aspects of ML development and maintenance that you have encountered while building and growing the Assembly platform?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Assembly?When is ML the wrong choice?What do you have planned for the future of Assembly?Contact Info@YouveGotFox on TwitterLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksAssembly AIPodcast.__init__ EpisodeLearn Python the Hard WayNLTKNLP == Natural Language ProcessingNLU == Natural Language UnderstandingSpeech RecognitionTensorflowr/machinelearningSciPyPyTorchJaxHuggingFaceRNN == Recurrent Neural NetworkCNN == Convolutional Neural NetworkLSTM == Long Short Term MemoryHidden Markov ModelsBaidu DeepSpeechCTC (Connectionist Temporal Classification) Loss ModelTwilioGrid SearchK80 GPUA100 GPUTPU == Tensor Processing UnitFoundation ModelsBLOOM Language ModelDALL-E 2The intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryThe majority of machine learning projects that you read about or work on are built around batch processes. The model is trained, and then validated, and then deployed, with each step being a discrete and isolated task. Unfortunately, the real world is rarely static, leading to concept drift and model failures. River is a framework for building streaming machine learning projects that can constantly adapt to new information. In this episode Max Halford explains how the project works, why you might (or might not) want to consider streaming ML, and how to get started building with River.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!Your host is Tobias Macey and today I’m interviewing Max Halford about River, a Python toolkit for streaming and online machine learningInterviewIntroductionHow did you get involved in machine learning?Can you describe what River is and the story behind it?What is "online" machine learning? What are the practical differences with batch ML?Why is batch learning so predominant?What are the cases where someone would want/need to use online or streaming ML?The prevailing pattern for batch ML model lifecycles is to train, deploy, monitor, repeat. What does the ongoing maintenance for a streaming ML model look like? Concept drift is typically due to a discrepancy between the data used to train a model and the actual data being observed. How does the use of online learning affect the incidence of drift?Can you describe how the River framework is implemented? How have the design and goals of the project changed since you started working on it?How do the internal representations of the model differ from batch learning to allow for incremental updates to the model state?In the documentation you note the use of Python dictionaries for state management and the flexibility offered by that choice. What are the benefits and potential pitfalls of that decision?Can you describe the process of using River to design, implement, and validate a streaming ML model? What are the operational requirements for deploying and serving the model once it has been developed?What are some of the challenges that users of River might run into if they are coming from a batch learning background?What are the most interesting, innovative, or unexpected ways that you have seen River used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on River?When is River the wrong choice?What do you have planned for the future of River?Contact InfoEmail@halford_max on TwitterMaxHalford on GitHubParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email [email protected]) with your story.To help other people find the show please leave a review on iTunes and tell your friends and co-workersLinksRiverscikit-multiflowFederated Machine LearningHogwild! Google PaperChip Huyen concept drift blog postDan Crenshaw Berkeley Clipper MLOpsRobustness PrincipleNY Taxi DatasetRiverTorchRiver Public RoadmapBeaver tool for deploying online modelsProdigy ML human in the loop labelingThe intro and outro music is from Hitman’s Lovesong feat. Paola Graziano by The Freak Fandango Orchestra/CC BY-SA 3.0

SummaryMachine learning is a transformative tool for the organizations that can take advantage of it. While the frameworks and platforms for building machine learning applications are becoming more powerful and broadly available, there is still a significant investment of time, money, and talent required to take full advantage of it. In order to reduce that barrier further Adam Oliner and Brian Calvert, along with their other co-founders, started Graft. In this episode Adam and Brian explain how they have built a platform designed to empower everyone in the business to take part in designing and building ML projects, while managing the end-to-end workflow required to go from data to production.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!Your host is Tobias Macey and today I’m interviewing Brian Calvert and Adam Oliner about Graft, a cloud-native platform designed to simplify the work of applying AI to business problemsInterviewIntroductionHow did you get involved in machine learning?Can you describe what Graft is and the story behind it?What is the core thesis of the problem you are targeting? How does the Graft product address that problem?Who are the personas that you are focused on working with both now in your early stages and in the future as you evolve the product?What are the capabilities that can be unlocked in different organizations by reducing the friction and up-front investment required to adopt ML/AI? What are the user-facing interfaces that you are focused on providing to make that adoption curve as shallow as possible? What are some of the unavoidable bits of complexity that need to be surfaced to the end user?Can you describe the infrastructure and platform design that you are relying on for the Graft product? What are some of the emerging "best practices" around ML/AI that you have been able to build on top of? As new techniques and practices are discovered/introduced how are you thinking about the adoption process and how/when to integrate them into the Graft product?What are some of the new engineering challenges that you have had to tackle as a result of your specific product?Machine learning can be a very data and compute intensive endeavor. How are you thinking about scalability in a multi-tenant system? Different model and data types can be widely divergent in terms of the cost (monetary, time, compute, etc.) required. How are you thinking about amortizing vs. passing through those costs to the end user?Can you describe the adoption/integration process for someone using Graft? Once they are onboarded and they have connected to their various data sources, what is the workflow for someone to apply ML capabilities to their problems?One of the challenges about the current state of ML capabilities and adoption is understanding what is possible and what is impractical. How have you designed Graft to help identify and expose opportunities for applying ML within the organization?What are some of the challenges of customer education and overall messaging that you are working through?What are the most interesting, innovative, or unexpected ways that you have seen Graft used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Graft?When is Graft the wrong choice?What do you have planned for the future of Graft?Contact InfoBrian LinkedInAdam LinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. P

SummaryIn order for a machine learning model to build connections and context across the data that is fed into it the raw data needs to be engineered into semantic features. This is a process that can be tedious and full of toil, requiring constant upkeep and often leading to rework across projects and teams. In order to reduce the amount of wasted effort and speed up experimentation and training iterations a new generation of services are being developed. Tecton first built a feature store to serve as a central repository of engineered features and keep them up to date for training and inference. Since then they have expanded the set of tools and services to be a full-fledged feature platform. In this episode Kevin Stumpf explains the different capabilities and activities related to features that are necessary to maintain velocity in your machine learning projects.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!Your host is Tobias Macey and today I’m interviewing Kevin Stumpf about the role of feature platforms in your ML engineering workflowInterviewIntroductionHow did you get involved in machine learning?Can you describe what you mean by the term "feature platform"? What are the components and supporting capabilities that are needed for such a platform?How does the availability of engineered features impact the ability of an organization to put ML into production?What are the points of friction that teams encounter when trying to build and maintain ML projects in the absence of a fully integrated feature platform?Who are the target personas for the Tecton platform? What stages of the ML lifecycle does it address?Can you describe how you have designed the Tecton feature platform? How have the goals and capabilities of the product evolved since you started working on it?What is the workflow for an ML engineer or data scientist to build and maintain features and use them in the model development workflow?What are the responsibilities of the MLOps stack that you have intentionally decided not to address? What are the interfaces and extension points that you offer for integrating with the other utilities needed to manage a full ML system?You wrote a post about the need to establish a DevOps approach to ML data. In keeping with that theme, can you describe how to think about the approach to testing and validation techniques for features and their outputs?What are the most interesting, innovative, or unexpected ways that you have seen Tecton/Feast used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Tecton?When is Tecton the wrong choice?What do you have planned for the future of the Tecton feature platform?Contact InfoLinkedIn@kevinmstumpf on Twitterkevinstumpf on GitHubParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?LinksTectonData Engineering Podcast EpisodeUber MichaelangeloFeature StoreSnowflakeData Engineering Pod

SummaryMachine learning is a data hungry activity, and the quality of the resulting model is highly dependent on the quality of the inputs that it receives. Generating sufficient quantities of high quality labeled data is an expensive and time consuming process. In order to reduce that time and cost Alex Ratner and his team at Snorkel AI have built a system for powering data-centric machine learning development. In this episode he explains how the Snorkel platform allows domain experts to create labeling functions that translate their expertise into reusable logic that dramatically reduces the time needed to build training data sets and drives down the total cost.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Your host is Tobias Macey and today I’m interviewing Alex Ratner about Snorkel AI, a platform for data-centric machine learning workflows powered by programmatic data labeling techniquesInterviewIntroductionHow did you get involved in machine learning?Can you describe what Snorkel AI is and the story behind it?What are the problems that you are focused on solving? Which pieces of the ML lifecycle are you focused on?How did your experience building the open source Snorkel project and working with the community inform your product direction for Snorkel AI? How has the underlying Snorkel project evolved over the past 4 years?What are the deciding factors that an organization or ML team need to consider when evaluating existing labeling strategies against the programmatic approach that you provide? What are the features that Snorkel provides over and above managing code execution across the source data set?Can you describe what you have built at Snorkel AI and how it is implemented? What are some of the notable developments of the ML ecosystem that had a meaningful impact on your overall product vision/viability?Can you describe the workflow for an individual or team who is using Snorkel for generating their training data set? How does Snorkel integrate with the experimentation process to track how changes to labeling logic correlate with the performance of the resulting model?What are some of the complexities involved in designing and testing the labeling logic? How do you handle complex data formats such as audio, video, images, etc. that might require their own ML models to generate labels? (e.g. object detection for bounding boxes)With the increased scale and quality of labeled data that Snorkel AI offers, how does that impact the viability of autoML toolchains for generating useful models?How are you managing the governance and feature boundaries between the open source Snorkel project and the business that you have built around it?What are the most interesting, innovative, or unexpected ways that you have seen Snorkel AI used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Snorkel AI?When is Snorkel AI the

SummaryDeep learning is a revolutionary category of machine learning that accelerates our ability to build powerful inference models. Along with that power comes a great deal of complexity in determining what neural architectures are best suited to a given task, engineering features, scaling computation, etc. Predibase is building on the successes of the Ludwig framework for declarative deep learning and Horovod for horizontally distributing model training. In this episode CTO and co-founder of Predibase, Travis Addair, explains how they are reducing the burden of model development even further with their managed service for declarative and low-code ML and how they are integrating with the growing ecosystem of solutions for the full ML lifecycle.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.Your host is Tobias Macey and today I’m interviewing Travis Addair about Predibase, a low-code platform for building ML models in a declarative formatInterviewIntroductionHow did you get involved in machine learning?Can you describe what Predibase is and the story behind it?Who is your target audience and how does that focus influence your user experience and feature development priorities?How would you describe the semantic differences between your chosen terminology of "declarative ML" and the "autoML" nomenclature that many projects and products have adopted? Another platform that launched recently with a promise of "declarative ML" is Continual. How would you characterize your relative strengths?Can you describe how the Predibase platform is implemented? How have the design and goals of the product changed as you worked through the initial implementation and started working with early customers?The operational aspects of the ML lifecycle are still fairly nascent. How have you thought about the boundaries for your product to avoid getting drawn into scope creep while providing a happy path to delivery?Ludwig is a core element of your platform. What are the other capabilities that you are layering around and on top of it to build a differentiated product?In addition to the existing interfaces for Ludwig you created a new language in the form of PQL. What was the motivation for that decision? How did you approach the semantic and syntactic design of the dialect?What is your vision for PQL in the space of "declarative ML" that you are working to define?Can you describe the available workflows for an individual or team that is using Predibase for prototyping and validating an ML model? Once a model has been deemed satisfactory, what is the path to production?How are you approaching governance and sustainability of Ludwig and Horovod while balancing your reliance on them in Predibase?What are some of the notable investments/improvements that you have made in Ludwig during your work of building Predibase?What are the most interesting, innovative, or unexpected ways that you have seen Predibase used?What are the most interesting, unexpected, o

SummaryMachine learning is a force multiplier that can generate an outsized impact on your organization. Unfortunately, if you are feeding your ML model garbage data, then you will get orders of magnitude more garbage out of it. The team behind Galileo experienced that pain for themselves and have set out to make data management and cleaning for machine learning a first class concern in your workflow. In this episode Vikram Chatterji shares the story of how Galileo got started and how you can use their platform to fix your ML data so that you can get back to the fun parts.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.Building good ML models is hard, but testing them properly is even harder. At Deepchecks, they built an open-source testing framework that follows best practices, ensuring that your models behave as expected. Get started quickly using their built-in library of checks for testing and validating your model’s behavior and performance, and extend it to meet your specific needs as your model evolves. Accelerate your machine learning projects by building trust in your models and automating the testing that you used to do manually. Go to themachinelearningpodcast.com/deepchecks today to get started!Your host is Tobias Macey and today I’m interviewing Vikram Chatterji about Galileo, a platform for uncovering and addressing data problems to improve your model qualityInterviewIntroductionHow did you get involved in machine learning?Can you describe what Galileo is and the story behind it?Who are the target users of the platform and what are the tools/workflows that you are replacing? How does that focus inform and influence the design and prioritization of features in the platform?What are some of the real-world impacts that you have experienced as a result of the kinds of data problems that you are addressing with Galileo?Can you describe how the Galileo product is implemented? What are some of the assumptions that you had formed from your own experiences that have been challenged as you worked with early design partners?The toolchains and model architectures of any given team is unlikely to be a perfect match across departments or organizations. What are the core principles/concepts that you have hooked into in order to provide the broadest compatibility? What are the model types/frameworks/etc. that you have had to forego support for in the early versions of your product?Can you describe the workflow for someone building a machine learning model and how Galileo fits across the various stages of that cycle? What are some of the biggest difficulties posed by the non-linear nature of the experimentation cycle in model development?What are some of the ways that you work to quantify the impact of your tool on the productivity and profit contributions of an ML team/organization?What are the most interesting, innovative, or unexpected ways that you have seen Galileo used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Galileo?When is Galileo the wrong choice?What do you have planned for the future of Galileo?Contact InfoLinkedInParting QuestionFrom your perspective, what is the biggest barrier to adoption of machine learning today?Closing AnnouncementsThank you for listening! Don’t forget to check out our other shows. The Data Engineering Podcast covers the latest on modern data management. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used.Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes.If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@

SummaryMachine learning has the potential to transform industries and revolutionize business capabilities, but only if the models are reliable and robust. Because of the fundamental probabilistic nature of machine learning techniques it can be challenging to test and validate the generated models. The team at Deepchecks understands the widespread need to easily and repeatably check and verify the outputs of machine learning models and the complexity involved in making it a reality. In this episode Shir Chorev and Philip Tannor explain how they are addressing the problem with their open source deepchecks library and how you can start using it today to build trust in your machine learning applications.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!Your host is Tobias Macey and today I’m interviewing Shir Chorev and Philip Tannor about Deepchecks, a Python package for comprehensively validating your machine learning models and data with minimal effort.InterviewIntroductionHow did you get involved in machine learning?Can you describe what Deepchecks is and the story behind it?Who is the target audience for the project? What are the biggest challenges that these users face in bringing ML models from concept to production and how does DeepChecks address those problems?In the absence of DeepChecks how are practitioners solving the problems of model validation and comparison across iteratiosn? What are some of the other tools in this ecosystem and what are the differentiating features of DeepChecks?What are some examples of the kinds of tests that are useful for understanding the "correctness" of models? What are the methods by which ML engineers/data scientists/domain experts can define what "correctness" means in a given model or subject area?In software engineering the categories of tests are tiered as unit -> integration -> end-to-end. What are the relevant categories of tests that need to be built for validating the behavior of machine learning models?How do model monitoring utilities overlap with the kinds of tests that you are building with deepchecks?Can you describe how the DeepChecks package is implemented? How have the design and goals of the project changed or evolved from when you started working on it?What are the assumptions that you have built up from your own experiences that have been challenged by your early users and design partners?Can you describe the workflow for an individual or team using DeepChecks as part of their model training and deployment lifecycle?Test engineering is a deep discipline in its own right. How have you approached the user experience and API design to reduce the overhead for ML practitioners to adopt good practices?What are the interfaces available for creating reusable tests and composing test suites together?What are the additional services/capabilities that you are providing in your commercial offering? How are you managing the governance and sustainability of

SummaryBuilding an ML model is getting easier than ever, but it is still a challenge to get that model in front of the people that you built it for. Baseten is a platform that helps you quickly generate a full stack application powered by your model. You can easily create a web interface and APIs powered by the model you created, or a pre-trained model from their library. In this episode Tuhin Srivastava, co-founder of Basten, explains how the platform empowers data scientists and ML engineers to get their work in production without having to negotiate for help from their application development colleagues.AnnouncementsHello and welcome to the Machine Learning Podcast, the podcast about machine learning and how to bring it from idea to delivery.Data powers machine learning, but poor data quality is the largest impediment to effective ML today. Galileo is a collaborative data bench for data scientists building Natural Language Processing (NLP) models to programmatically inspect, fix and track their data across the ML workflow (pre-training, post-training and post-production) – no more excel sheets or ad-hoc python scripts. Get meaningful gains in your model performance fast, dramatically reduce data labeling and procurement costs, while seeing 10x faster ML iterations. Galileo is offering listeners a free 30 day trial and a 30% discount on the product there after. This offer is available until Aug 31, so go to themachinelearningpodcast.com/galileo and request a demo today!Do you wish you could use artificial intelligence to drive your business the way Big Tech does, but don’t have a money printer? Graft is a cloud-native platform that aims to make the AI of the 1% accessible to the 99%. Wield the most advanced techniques for unlocking the value of data, including text, images, video, audio, and graphs. No machine learning skills required, no team to hire, and no infrastructure to build or maintain. For more information on Graft or to schedule a demo, visit themachinelearningpodcast.com/graft today and tell them Tobias sent you.Predibase is a low-code ML platform without low-code limits. Built on top of our open source foundations of Ludwig and Horovod, our platform allows you to train state-of-the-art ML and deep learning models on your datasets at scale. Our platform works on text, images, tabular, audio and multi-modal data using our novel compositional model architecture. We allow users to operationalize models on top of the modern data stack, through REST and PQL – an extension of SQL that puts predictive power in the hands of data practitioners. Go to themachinelearningpodcast.com/predibase today to learn more and try it out!Your host is Tobias Macey and today I’m interviewing Tuhin Srivastava about Baseten, an ML Application Builder for data science and machine learning teamsInterviewIntroductionHow did you get involved in machine learning?Can you describe what Baseten is and the story behind it?Who are the target users for Baseten and what problems are you solving for them?What are some of the typical technical requirements for an application that is powered by a machine learning model? In the absence of Baseten, what are some of the common utilities/patterns that teams might rely on?What kinds of challenges do teams run into when serving a model in the context of an application?There are a number of projects that aim to reduce the overhead of turning a model into a usable product (e.g. Streamlit, Hex, etc.). What is your assessment of the current ecosystem for lowering the barrier to product development for ML and data science teams?Can you describe how the Baseten platform is designed? How have the design and goals of the project changed or evolved since you started working on it?How do you handle sandboxing of arbitrary user-managed code to ensure security and stability of the platform?How did you approach the system design to allow for mapping application development paradigms into a structure that was accessible to ML professionals?Can you describe the workflow for building an ML powered application?What types of models do you support? (e.g. NLP, computer vision, timeseries, deep neural nets vs. linear regression, etc.) How do the monitoring requirements shift for these different model types?What other challenges are presented by these different model types?What are the limitations in size/complexity/operational requirements that you have to impose to ensure a stable platform?What is the process for deploying model updates?For organizations that are relying on Baseten as a prototyping platform, what are the options for taking a successful application and handing it off to a product team for further customization?What are the most interesting, innovative, or unexpected ways that you have seen Baseten used?What are the most interesting, unexpected, or challenging lessons that you have learned while working on Baseten?When is Baseten the wrong choice?What do you have planned for the f

Hello, and welcome to the Machine Learning Podcast. I’m your host, Tobias Macey. You might know me from the Data Engineering Podcast or the Python Podcast.__init__. If you work with machine learning and AI, or you’re curious about it and want to learn more, then this show is for you. We’ll go beyond the esoteric research and flashy headlines and find out how machine learning is making an impact on the world and creating value for business. Along the way we’ll be joined by the researchers, engineers, and entrepreneurs who are shaping the industry. So go to themachinelearningpodcast.com today to subscribe and stay informed on how ML/AI are being used, how it works, and how to go from idea to production.Support The Machine Learning Podcast