Machine Learning Engineered

Alex Watson is the co-founder and CEO of Gretel.ai, a startup that offers APIs for creating anonymized and synthetic datasets. Previously he was the founder of Harvest.ai, whose product Macie, an analytics platform protecting against data breaches, was acquired by AWS.Learn more about Alex and Gretel AI:http://gretel.aiEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 Introducing Alex Watson03:45 How Alex was first exposed to programming05:00 Alex's experience starting Harvest AI, getting acquired by AWS, and integrating their product at massive scale21:20 How Alex first saw the opportunity for Gretel.ai24:20 The most exciting use-cases for synthetic data28:55 Theoretical guarantees of anonymized data with differential privacy36:40 Combining pre-training with synthetic data38:40 When to anonymize data and when to synthesize it41:25 How Gretel's synthetic data engine works44:50 Requirements of a dataset to create a synthetic version49:25 Augmenting datasets with synthetic examples to address representation bias52:45 How Alex recommends teams get started with Gretel.ai59:00 Expected accuracy loss from training models on synthetic data01:03:15 Biggest surprises from building Gretel.ai01:05:25 Organizational patterns for protecting sensitive data01:07:40 Alex's vision for Gretel's data catalog01:11:15 Rapid fire questionsLinks:Gretel.ai BlogNetFlix Cancels Recommendation Contest After Privacy LawsuitGreylock - The Github of DataImproving massively imbalanced datasets in machine learning with synthetic dataDeep dive on generating synthetic data for HealthcareGretel’s New Synthetic Performance ReportThe...

Radek Osmulski is a fully self-taught machine learning engineer. After getting tired of his corporate job, he taught himself programming and started a new career as a Ruby on Rails developer. He then set out to learn machine learning. Since then, he's been a Fast AI International Fellow, become a Kaggle Master, and is now an AI Data Engineer on the Earth Species Project.Learn more about Radek:https://www.radekosmulski.comhttps://twitter.com/radekosmulskiEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 How Radek got interested in programming and computer science09:00 How Radek taught himself machine learning26:40 The skills Radek learned from Fast AI39:20 Radek's recommendations for people learning ML now51:30 Why Radek is writing a book01:01:20 Radek's work at the Earth Species Project01:10:15 How the ESP collects animal language data01:21:05 Rapid fire questionsLinks:Radek's Book "Meta-Learning"Andrew Ng ML CourseraFast AIUniversal Language Model Fine-tuning for Text ClassificationHow to do Machine Learning EfficientlyNPR - Two Heartbeats a MinuteEarth Species ProjectA Guide to the Good LifeThe Origin of WealthMake TimeYou Are Here

Rodrigo Rivera is a machine learning researcher at the Advanced Data Analytics in Science and Engineering Group at Skoltech and technical director of Samsung Next. He's previously been in data science and research leadership roles at companies all around the world including Rocket Internet and Philip-Morris.Learn more about Rodrigo:https://rodrigo-rivera.com/https://twitter.com/rodrigorivrEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:03:00 How Rodrigo got started in computer science and started his first company10:40 Rodrigo's experiences leading data science teams at Rocket Internet and PMI26:15 Leaving industry to get a PhD in machine learning28:55 Data science collaboration between business and academia32:45 Rodrigo's research interest in time series data39:25 Topological data analysis45:35 Framing effective research as a startup48:15 Neural Prophet01:04:10 The potential future of Julia for numerical computing01:08:20 Most exciting opportunities for ML in industry01:15:05 Rodrigo's advice for listeners01:17:00 Rapid fire questionsLinks:Rodrigo's Google ScholarAdvanced Data Analytics in Science and Engineering GroupNeural ProphetM-CompetitionsMachine Learning RefinedFoundations of Machine LearningA First Course in Machine Learning

Dan Jeffries is the chief technical evangelist at Pachyderm, a leading data science platform. He's a prominent writer and speaker on all things related to the future. He's been in software for over two decades, many of those at Redhat, and is the founder of the AI Infrastructure Alliance and Practical AI Ethics.Learn more about Dan:https://twitter.com/Dan_Jeffries1https://medium.com/@dan.jeffriesEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 How Dan got started in computer science06:50 What Dan is most excited about in AI14:45 Where we are in the adoption curve of ML20:40 The "Canonical Stack" of ML32:00 Dan's goal for the AI Infrastructure Alliance40:55 "Problems that ML startups don't know they're going to have"49:00 Closed vs open source tools in the Canonical Stack01:00:05 Building out the "boring" part of the infrastructure to enable exciting applications01:08:40 Dan's practical approach to AI Ethics01:23:50 Rapid fire questionsLinks:PachydermAI Infrastructure AlliancePractical AI Ethics AllianceRise of the Canonical Stack in Machine LearningRise of AI - The Age of AI in 2030Google MagentaAlphaGo DocumentaryThinking in BetsA History of the World in 6 GlassesSuper-Thinking

Willem Pienaar is the co-creator of Feast, the leading open source feature store, which he leads the development of as a tech lead at Tecton. Previously, he led the ML platform team at Gojek, a super-app in Southeast Asia.Learn more:https://twitter.com/willpienaarhttps://feast.dev/Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 How Willem got started in computer science03:40 Paying for college by starting an ISP05:25 Willem's experience creating Gojek's ML platform21:45 Issues faced that led to the creation of Feast26:45 Lessons learned building Feast33:45 Integrating Feast with data quality monitoring tools40:10 What it looks like for a team to adopt Feast44:20 Feast's current integrations and future roadmap46:05 How a data scientist would use Feast when creating a model49:40 How the feature store pattern handles DAGs of models52:00 Priorities for a startup's data infrastructure55:00 Integrating with Amundsen, Lyft's data catalog57:15 The evolution of data and MLOps tool standards for interoperability01:01:35 Other tools in the modern data stack01:04:30 The interplay between open and closed source offeringsLinks:Feast's GithubGojek Data Science BlogData Build Tool (DBT)Tensorflow Data Validation (TFDV)A State of FeastGoogle BigQueryLyft AmundsenCortexKubeflowMLFlow

Benedikt Koller is a self-professed "Ops guy", having spent over 12 years working in roles such as DevOps engineer, platform engineer, and infrastructure tech lead at companies like Stylight and Talentry in addition to his own consultancy KEMB. He's recently dove head first into the world of ML, where he hopes to bring his extensive ops knowledge into the field as the co-founder of Maiot, the company behind ZenML, an open source MLOps framework.Learn more:https://zenml.io/https://maiot.io/Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 Introducing Benedikt Koller05:30 What the "DevOps revolution" was10:10 Bringing good Ops practices into ML projects30:50 Pivoting from vehicle predictive analytics to open source ML tooling34:35 Design decisions made in ZenML39:20 Most common problems faced by applied ML teams49:00 The importance of separating configurations from code55:25 Resources Ben recommends for learning Ops57:30 What to monitor in an ML pipelines01:00:45 Why you should run experiments in automated pipelines01:08:20 The essential components of an MLOps stack01:10:25 Building an open source business and what's next for ZenML01:20:20 Rapid fire questionsLinks:ZenML's GitHubMaiot BlogThe Twelve Factor App12 Factors of reproducible Machine Learning in productionSeldonPachydermKubeFlowSomething Deeply HiddenThe Expanse SeriesThe Three Body ProblemExtreme Ownership

Josh Albrecht is the co-founder and CTO of Generally Intelligent, an independent research lab investigating the fundamentals of learning across humans and machines. Previously, he was the lead data architect at Addepar, CTO of CloudFab, and CTO of Sourceress, which Generally Intelligent is a pivot from.Learn more about Josh:http://joshalbrecht.com/http://generallyintelligent.ai/Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://www.cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 Introducing Josh Albrecht03:30 How Josh got started in computer science06:35 Josh's first two startup attempts09:15 The tech behind Sourceress, an AI recruiting platform16:10 Pivoting from Sourceress to Generally Intelligent, an AI research lab23:50 How Josh defines "general intelligence"28:35 Why Josh thinks self-supervised learning is the current most promising research area36:15 Generally Intelligent's immediate research roadmap: BYOL, simulated environments59:20 How Josh thinks about creating an optimal research environment01:11:35 The "why" behind starting an independent research lab01:13:30 AI alignment01:17:00 Rapid fire questionsLinks:Bootstrap your own latent: A new approach to self-supervised LearningUnderstanding self-supervised and contrastive learning with "Bootstrap Your Own Latent" (BYOL)BYOL works even without batch statisticsGenerally Intelligent PodcastConsequences of Misaligned AIWhy We SleepPeak

Elena Samuylova and Emeli Dral are the co-founders of Evidently AI, where they build open source tools to analyze and monitor machine learning models. Elena was previously the head of the startup ecosystem at Yandex, director of business development at their data factory and chief product officer at Mechanica AI. Emeli was previously a data scientist at Yandex, chief data scientist at the data factory and Mechanica AI in addition to teaching machine learning both online and at multiple universities.Learn more about Elena, Emeli, and Evidently AI:https://evidentlyai.com/https://twitter.com/elenasamuylovahttps://twitter.com/EmeliDralEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:15 How Emeli and Elena each got started in data science07:10 Applying machine learning across a wide variety of industries at the Yandex Data Factory14:55 Using ML for industrial process improvement23:35 Challenges encountered in industrial ML and technical solutions27:15 The huge opportunity for ML in manufacturing34:35 How to ensure safety when using models in physical systems37:40 Why they started working on tools for data and ML monitoring42:50 Different kinds of data drift and how to address them48:25 Common mistakes ML teams make in monitoring55:25 Features of Evidently AI's library57:35 Building open source software01:02:25 Technical roadmap for Evidently01:05:50 Monitoring complex data01:08:50 Business roadmap for Evidently01:11:35 Rapid fire questionsLinks:Evidently on GithubEvidently AI's BlogThinking Fast and SlowFlowDoing Good Better

Harikrishna Narayanan is the co-founder of a YC-backed stealth startup. He was previously a Principal Engineer at Yahoo, a Director in Workday's Machine Learning organization, and holds an M.S. from Georgia Tech.Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:45 How Hari got started in computer science and machine learning06:00 Making the transition from IC to manager14:35 What it means to be an effective engineering manager19:20 Differences in managing machine learning vs traditional software teams24:30 The importance of explaining complicated topics simply30:15 How he thinks about hiring for data science and machine learning36:50 Mistakes Workday made as it adopted machine learning41:50 Essential skills for machine learning engineers54:05 Why the future of AI is augmentation, not automation58:30 His experience so far with YC01:02:00 Rapid fire questionsLinks:Growth MindsetThe Feynman TechniqueRadical CandorTrillion Dollar CoachMultipliersGood to GreatThe First 90 DaysCrossing the ChasmZero to OneThe Lean StartupThe Hard Thing About Hard ThingsSapiensA Short History of Nearly EverythingOn IntelligencePrediction MachinesAlgorithms to Live By<a...

Learn more about the ML Ops Community: https://mlops.community/Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: https://cyou.ai/newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:45 Intro04:10 How I got into data science and machine learning08:25 My experience working as an ML engineer and starting the podcast12:15 Project management methods for machine learning20:50 ML job roles are trending towards more specialization26:15 ML tools enable collaboration between roles and encode best practices34:00 Data privacy, security, and provenance as first class considerations39:30 The future of managed ML platforms and cloud providers49:05 What I've learned about building a career in ML engineering54:10 Dealing with information overloadLinks:Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in ProductionThe Third Wave Data ScientistPractical ML Ops // Noah Gift // MLOps Coffee SessionsBuilding a Post-Scarcity Future using Machine Learning with Pavle Jeremic (Aether Bio)SRE for ML Infra // Todd Underwood // MLOps Coffee SessionsLuigi Patruno on the ML Ops Community podcastLuigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"

Pavle Jeremic is the founder and CEO of Aether Biomachines, one of the most exciting ML-powered startups I've come across. His mission is to solve scarcity and Aether is the first step towards that. He was recently featured in Forbes' 30 under 30 in Manufacturing and holds a B.S. in Biomolecular Engineering from UC Santa Cruz.Learn more:Aether BiomachinesEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:45 Pavle Jeremic05:20 How Pavle was introduced to computer science and programming08:00 Solving scarcity from first principles23:20 How Aether contributes to the post-scarcity future29:30 What enzymatic reaction data looks like37:20 Using deep learning to figure out what enzymatic experiments to run next39:45 How Aether runs thousands of experiments at a time47:00 What the current bottleneck of the system is53:15 The evolution of ML models at Aether59:00 Gaps in existing ML infrastructure solutions01:03:30 Why Aether is releasing some of their data for a competition01:06:50 The upcoming roadmap for Aether01:09:30 Rapid fire questionsLinks:Founders First Interview - Making Alchemy RealDeepChemEngines of CreationRama Series

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:50 Josh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in Production21:48 Shreya Shankar: Lessons learned after a year of putting ML into production34:44 Luigi Patruno: ML in Production, Adding Business Value with Data Science, "Code 2.0"53:28 Music Information Retrieval at Spotify and the Future of ML Tooling with Andreas Jansson of Replicate

Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/

Andreas Jansson is the co-founder of Replicate, a version control tool for machine learning. He holds a PhD from City University of London in Music Informatics and was previously a machine learning engineer at Spotify, researching and applying algorithms for music information retrieval.Learn more about Andreas:https://replicate.ai/https://www.linkedin.com/in/janssonandreas/Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:30 Andreas Jansson07:30 Overview of music information retrieval (MIR)13:30 Why use spectrograms and not raw audio?19:55 The potential for transformers in MIR22:45 Most exciting applications for ML in MIR29:20 Challenges in putting ML into production36:45 What Andreas imagines for the future of ML tools41:45 Why he's building a tool for ML version control (http://replicate.ai/)52:55 What Replicate enables via integration or as a platform01:02:55 Learnings from doing customer discovery for Replicate01:14:10 "Github for ML models and data"01:22:30 Rapid fire questionsLinks:WaveNet: a generative model for raw audioSinging Voice Separation with Deep U-Net CNNsJoint Singing Voice Separation and F0 Estimation with Deep U-Net ArchitecturesarXiv VanityReplicateReplicate's Discord

Luigi is the director of data science at 2U, where he leads a team in developing ML models and infrastructure to predict student success outcomes. He's also the founder of ML in Production, a blog and newsletter that helps readers build, deploy, and run ML systems.Learn more about Luigi:https://mlinproduction.com/https://twitter.com/mlinproductionEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:45 Luigi Patruno04:50 How can ML teams be more rigorous in their engineering practices?10:25 Best practices for monitoring and logging ML systems18:00 Adding business value with data science37:10 Most valuable types of tools for ML in production43:15 What an ideal data pipeline setup looks like47:50 Unbundling the "Data Scientist" role50:35 The future of building software: "Code 2.0"59:45 Most valuable skills for the future01:10:15 Learnings from writing his blog "ML in Production"01:15:00 Rapid fire questionsLinks:Luigi's interview on DatacastUltimate Guide to Deploying ML ModelsMaximizing Business Impact with Machine LearningTwo Types of Companies Using MLThe AI Hierarchy of NeedsJosh Tobin: Research at OpenAI, Full Stack Deep Learning, ML in ProductionMachine Learning is Forcing Software Development to EvolveML Street Talk #29: GPT-3, Prompt Engineering, Trading, AI Alignment, IntelligenceBuilding Machine Learning Powered ApplicationsHow to Change Your MindThe War of Art<a...

Shawn Wang formerly worked in finance as a derivatives trader and equity analyst before burning out and pivoting towards tech. He's a prolific blogger who goes under the pseudonym "swyx" and recently published the excellent Coding Career Handbook. He's a graduate of Free Code Camp and Full Stack Academy now working at AWS as a Senior Developer Advocate.Learn more about Shawn:https://swyx.io/https://www.learninpublic.org/https://twitter.com/swyxEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:45 swyx is back!05:25 How his book has been received so far11:35 Why and how to negotiate your salary24:10 Getting started in public speaking, giving talks at meetups and conferences35:45 The role of luck in your career and how to create it51:15 Biggest is not best, best *for me *****is best59:20 Why swyx angel-invested in Circle01:12:00 On Randy Pausch's Time Management lecture01:18:00 Using open source to accelerate your coding skill01:20:00 Handling information overload and enhancing retention with note taking01:27:20 What swyx does in his job as a Developer Advocate and why you should consider non-coding roles01:37:30 swyx's new podcast Career Chats (https://careerchats.transistor.fm/)Links:swyx's first ML Engineered appearanceswyx's book Coding Career HandbookHow to Create LuckNotes on Time Management from a Dying ProfessorBuilding a Second BrainSimpleNoteswyx's new podcast with Randall Kanna "Career Chats"

Yannic Kilcher is PhD candidate at ETH Zurich researching deep learning, structured learning, and optimization for large and high-dimensional data. He produces videos on his enormously popular Youtube channel breaking down recent ML papers.Follow Yannic on Twitter: https://twitter.com/ykilcherCheck out Yannic's excellent Youtube channel: https://www.youtube.com/channel/UCZHmQk67mSJgfCCTn7xBfewListen to the ML Street Talk podcast: https://podcasts.apple.com/us/podcast/machine-learning-street-talk/id1510472996Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:40 Yannic Kilcher07:05 Research for his PhD thesis and plans for the future12:05 How he produces videos for his enormously popular Youtube channel21:50 Yannic's research process: choosing what to read and how he reads for understanding27:30 Why ML conference peer review is broken and what a better solution looks like45:20 On the field's obsession with state of the art48:30 Is deep learning is the future of AI? Is attention all you need?56:10 Is AI overhyped right now?01:01:00 Community Questions01:13:30 Yannic flips the script and asks me about what I do01:25:30 Rapid fire questionsLinks:Yannic's amazing Youtube ChannelYannic's Google ScholarYannic's Community Discord ChannelOn the Measure of Intelligence: arXiv paper and Yannic's video seriesHow I Read a Paper: Facebook's DETR (Video Tutorial)An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (Paper Explained)Zero to OneThe Gulag Archipelago

Zak Slayback is a principal at 1517 Fund, a venture capital fund that prioritizes working with dropouts. He wrote the excellent book "How to Get Ahead", one of my most recommended books on careers, and runs Get Ahead Labs where he teaches how to write outstanding cold emails.Learn more about Zak:https://zakslayback.com/https://www.1517fund.com/Every Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAISubscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bit.ly/mle-surveyTake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Timestamps:02:35 Zak Slayback04:45 Using opportunity cost, signaling theory, and incentives to accelerate your career (https://zakslayback.com/frameworks-success-opportunity-cost/)14:35 How to set career goals (https://zakslayback.com/ambition-mapping/)20:15 Rene Girard and Mimetic Desire24:30 The difference between a mentor, a coach/consultant, and an advisor (https://zakslayback.com/whats-difference-mentors-advisors-coaches/)35:40 Finding a mentor (https://zakslayback.com/professional-mentor-dream-job/)44:30 Fighting mental blocks against reaching out to potential mentors47:30 Why you should start a personal website (https://zakslayback.com/why-start-a-website/)56:15 What the most important "meta-skills" are and how to stack talents01:05:35 Most over-looked sections of the book01:09:00 The future of higher education: the new 95 theses from 1517 Fund (https://medium.com/1517/a-new-95-ec071200d98f)01:23:05 What Zak thinks the most exciting trends in technology are01:35:15 Rapid fire questionsLinks:The End of School and Building a Valuable Skillset with Zak SlaybackDeschool Yourself and Find Your Focus – With Zak SlaybackZak's book - How to Get Ahead (highly recommended!)Ambition MappingRene Girard and Mimetic Desire<a

Letitia Parcalabescu is a PhD candidate at the University of Heidelberg focused on multi-modal machine learning, specifically with vision and language.Learn more about Letitia:https://www.cl.uni-heidelberg.de/~parcalabescu/https://www.youtube.com/channel/UCobqgqE4i5Kf7wrxRxhToQAEvery Thursday I send out the most useful things I’ve learned, curated specifically for the busy machine learning engineer. Sign up here: http://bitly.com/mle-newsletterFollow Charlie on Twitter: https://twitter.com/CharlieYouAITake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenComments? Questions? Submit them here: http://bitly.com/mle-surveyTimestamps:01:30 Follow Charlie on Twitter (https://twitter.com/CharlieYouAI)02:40 Letitia Parcalabescu03:55 How she got started in CS and ML07:20 What is multi-modal machine learning? (https://www.youtube.com/playlist?list=PLpZBeKTZRGPNKxoNaeMD9GViU_aH_HJab)16:55 Most exciting use-cases for ML20:45 The 5 stages of machine understanding (https://www.youtube.com/watch?v=-niprVHNrgI)23:15 The future of multi-modal ML (GPT-50?)27:00 The importance of communicating AI breakthroughs to the general public37:40 Positive applications of the future “GPT-50”43:35 Letitia’s CVPR paper on phrase grounding (https://openaccess.thecvf.com/content_CVPRW_2020/papers/w56/Parcalabescu_Exploring_Phrase_Grounding_Without_Training_Contextualisation_and_Extension_to_Text-Based_CVPRW_2020_paper.pdf)53:15 ViLBERT: is attention all you need in multi-modal ML? (https://arxiv.org/abs/1908.02265)57:00 Preventing “modality dominance”01:03:25 How she keeps up in such a fast-moving field01:10:50 Why she started her AI Coffee Break YouTube Channel (https://www.youtube.com/c/AICoffeeBreakwithLetitiaParcalabescu/)01:18:10 Rapid fire questionsLinks:AI Coffee Break Youtube ChannelExploring Phrase Grounding without Training<a href="https://www.youtube.com/playlist?list=PLpZBeKTZRGPNKxoNaeMD9GViU_aH_HJab" rel="noopener noreferrer"...

Moin Nadeem is a masters student at MIT, where he studies natural language generation. His research interests broadly include natural language processing, information retrieval, and software systems for machine learning.Learn more about Moin:https://moinnadeem.com/https://twitter.com/moinnadeemWant to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletterComments? Questions? Submit them here: http://bit.ly/mle-surveyFollow Charlie on Twitter: https://twitter.com/CharlieYouAITake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenTimestamps:01:35 Follow Charlie on Twitter (https://twitter.com/CharlieYouAI)03:10 How Moin got started in computer science05:50 Using ML to identify depression on Twitter in high school11:00 Building a system to track phone locations on MIT’s campus14:35 Specializing in NLP17:20 Building an end-to-end fact-checking system (https://www.aclweb.org/anthology/N19-4014/)25:15 Predicting statement stance with neural multi-task learning (https://www.aclweb.org/anthology/D19-6603/)27:20 Is feature engineering in NLP dead?29:40 Reconciling language models with existing knowledge graphs35:20 How advances in AI hardware will affect NLP research (crazy!)47:25 Moin’s research into sampling algorithms for natural language generation (https://arxiv.org/abs/2009.07243)57:10 Under-rated areas of ML research01:00:10 How research works at MIT CSAIL01:04:35 How Moin keeps up in such a fast-moving field01:11:30 Starting the MIT Machine Intelligence Community01:16:30 Rapid Fire QuestionsLinks:FAKTA: An Automatic End-to-End Fact Checking SystemStereoSet: Measuring stereotypical bias in pretrained language modelsNeural Multi-Task Learning for Stance PredictionRich Sutton - The Bitter LessonA Systematic Characterization of Sampling Algorithms for Open-ended Language GenerationStrategies for Pre-training Graph Neural NetworksTransformers For Image Recognition at Scale<a href="https://www.cerebras.net/product/"...

Peiyuan Liao is the youngest Chinese Kaggle grandmaster at only 20 years old with numerous gold medals and 1st, 2nd, and 3rd place finishes. He helped research two deep learning papers while in high school and now researches adversarial attacks on graph neural networks at Carnegie Mellon.Learn more about Peiyuan:https://liaopeiyuan.github.io/https://www.kaggle.com/alexanderliaoWant to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletterComments? Questions? Submit them here: http://bit.ly/mle-surveyFollow Charlie on Twitter: https://twitter.com/CharlieYouAITake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenTimestamps:03:20 How Peiyuan was first exposed to CS and ML06:45 Researching deep learning in high school10:30 Researching graph neural networks at Carnegie Mellon (https://arxiv.org/abs/2009.13504)20:30 How he keeps up with the field and gets research ideas24:05 Research tools he uses31:30 Advice for Kaggle beginners34:30 How Peiyuan first approaches a new Kaggle competition40:15 His team's 3rd-place solution to the 2020 Google Landmark Recognition Challenge (https://arxiv.org/abs/2010.05350)50:30 How he approached the Global Wheat Detection challenge (https://www.kaggle.com/c/global-wheat-detection/discussion/175961)56:40 How he decides to quit a Kaggle competition59:25 The difference between him and the average Kaggler01:03:20 Contributing to open source projects01:06:00 Rapid Fire QuestionsLinks:CAE-ADMM: Implicit Bitrate Optimization via ADMM-based Pruning in Compressive AutoencodersGraph Adversarial Networks: Protecting Information against Adversarial AttacksPeiyuan's Kaggle ProfileOpen Neural Network Exchange (ONNX)Apache TVMDeformable Convolutional NetworksGoogle JAXRobert HarperGoogle Landmark Recognition 2020 Competition Third Place SolutionArcFace: Additive Angular Margin...

Shreya Shankar is a Machine Learning Engineer at Viaduct AI. She's a master's student at Stanford and has previously worked at Facebook and Google Brain. She writes some truly excellent articles about machine learning on her personal blog, https://www.shreya-shankar.com/Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: http://bit.ly/mle-newsletterComments? Questions? Submit them here: http://bit.ly/mle-surveyFollow Charlie on Twitter: https://twitter.com/CharlieYouAITake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenTimestamps:01:30 Follow Charlie on Twitter (http://twitter.com/charlieyouai)02:40 How Shreya got started in CS06:00 Choosing to concentrate in systems in undergrad (https://www.shreya-shankar.com/systems/)12:25 Research at Google Brain on fooling humans with adversarial examples (http://papers.nips.cc/paper/7647-adversarial-examples-that-fool-both-computer-vision-and-time-limited-humans.pdf)18:00 Deciding to go into industry instead of pursuing a PhD (https://www.shreya-shankar.com/new-grad-advice/)19:35 Why is putting ML into production so hard? (https://www.shreya-shankar.com/making-ml-work/)25:00 Best of the research graveyard29:05 Checklist for building an ML model for production34:10 Ensuring reproducibility39:25 Back to the checklist44:25 PM for ML engineering48:50 Monitoring ML deployments53:50 Fighting ML bias58:45 Feature engineering best practices01:02:30 Remote collaboration on data science projects01:07:45 AI Saviorism (https://www.shreya-shankar.com/ai-saviorism/)01:17:40 Rapid Fire QuestionsLinks:Why you should major in systemsAdversarial Examples that Fool Both Computer Vision and Time-Limited HumansChoosing between a PhD and industry for new computer science graduatesReflecting on a year of making machine learning actually usefulGet rid of AI Saviorism<a href="https://dataintensive.net/" rel="noopener noreferrer"

Josh Tobin holds a CS PhD from UC Berkeley, which he completed in four years while also working at OpenAI as a research scientist. His focus was on robotic perception and control, and contributed to the famous Rubik's cube robot hand video. He co-organizes the phenomenal Full Stack Deep Learning course and is now working on a new stealth startup.Learn more about Josh:http://josh-tobin.com/https://twitter.com/josh_tobin_Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: https://mlengineered.ck.page/943aa3fd46Comments? Questions? Submit them here: https://charlie266.typeform.com/to/DA2j9Md9Follow Charlie on Twitter: https://twitter.com/CharlieYouAITake the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenTimestamps:01:32 Follow Charlie on Twitter (twitter.com/charlieyouai)02:43 How Josh got started in CS and ML11:05 Why Josh worked on ML for robotics15:03 ML for Robotics research at OpenAI28:20 Josh's research process34:56 Why putting ML into production is so difficult44:46 What Josh thinks the ML Ops landscape will look like49:49 Common mistakes that production ML teams and companies make53:11 How ML systems will be built in the future59:37 The most valuable skills that ML engineers should develop01:03:50 Rapid Fire QuestionsLinksFull Stack Deep LearningDomain Randomization for Transferring Deep Neural Networks from Simulation to the Real WorldDomain Randomization and Generative Models for Robotic GraspingDeepMind Generative Query Network (GQN) paperGeometry Aware Neural RenderingJosh's PhD ThesisOpenAI Rubik's Cube Robot Hand videoWeights and Biases interview with JoshBuilding Data Intensive ApplicationsCreative Selection

Sanyam Bhutani is a Machine Learning Engineer at H2O.ai and host of the Chai Time Data Science Show.Learn more about Sanyam:Website: https://sanyambhutani.com/YouTube: https://www.youtube.com/c/ChaiTimeDataScienceChai Time Data Science: https://chaitimedatascience.com/Want to level-up your skills in machine learning and software engineering? Join the ML Engineered Newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(03:00) How Sanyam got started in CS and ML(16:20) Getting a CV internship after freshman year(20:30) Having a vision of using AI to help the masses of India(23:25) FastAI international fellowship(27:40) Interviews with Machine Learning Heroes(36:50) Frustration while training ML models(43:35) Interviewing Jeremy Howard(46:00) Creating ML content that resonates(01:01:00) Working at h2o.ai and making an ML course(01:11:00) Exciting opportunities in the field now(01:21:20) Rapid fire questions(01:26:35) OutroLinks:Interviews with Machine Learning HeroesSanyam's Interview with Jeremy HowardFastAIAndrew Ng ML CourseHigh Performance PythonThe Subtle Art of Not Giving a FuckH2O.ai Online Courses

Devon Bernard is an incredible full-stack engineer, manager, and entrepreneur. He's co-founded multiple companies including FlowActive, Jowl, and Rollio in addition to holding top engineering roles at Enlitic, Axgen, and now Somml.Learn more about Devon: https://www.linkedin.com/in/devonbernard/Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(00:00) Intro(02:00) How he got started in CS(03:03) Working for GOOG and MSFT while running 2 startups(05:10) Learning to program(10:25) How he got started in entrepreneurship(17:30) Building an animal crossing trading exchange(21:37) Designing scalable and maintainable backends(25:55) Does he use formal design methods (DDD)?(28:24) What makes for a great engineer?(36:43) Functional programming(39:43) Increasing productivity of engineering teams(45:56) Managing up as an individual contributor(49:59) Consulting advice(01:04:20) Health-tech startups(01:19:27) Exciting opportunities outside of healthcare(01:28:34) Rapid Fire QuestionsLinks:Never Split the DifferenceRadical Candor

Catherine Yeo is a Harvard undergrad studying Computer Science. She's previously worked for Apple, IBM, and MIT CSAIL in AI research and engineering roles. She writes about machine learning in Towards Data Science and in her new publication Fair Bytes.Learn more about Catherine: http://catherineyeo.tech/Read Fair Bytes: http://fairbytes.org/Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge: https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(02:48) How she was first exposed to CS and ML(07:06) Teaching a high school class on AI fairness(10:12) Definition of AI fairness(16:14) Adverse outcomes if AI bias is never addressed(22:50) How do "de-biasing" algorithms work?(27:42) Bias in Natural Language Generation(36:46) State of AI fairness research(38:22) Interventions needed?(43:18) What can individuals do to reduce model bias?(45:28) Publishing Fair Bytes(52:42) Rapid Fire QuestionsLinks:Defining and Evaluating Fair Natural Language GenerationMan is to Computer Programmer as Woman is to Homemaker?Gender ShadesGPT-3 Paper: Language Models are Few Shot LearnersHow Biased is GPT-3?Reading List for Fairness in AI TopicsMachine Learning’s Obsession with Kids’ TV Show Characters

Charles Yang is an EECS masters student at UC Berkeley focusing on AI and dynamical systems. He writes the excellent Machine Learning For Science newsletter where he showcases a wide range of use cases for machine learning in scientific research and engineering. Learn more about Charles:Website: https://charlesxjyang.github.io/Google Scholar: https://scholar.google.com/citations?user=BYOREdwAAAAJ&amp;hl=enML4Sci Newsletter (Highly Recommended!): https://ml4sci.substack.com/Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge:&nbsp;https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://www.mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(02:08) Getting started in material science and machine learning(08:58) "ImageNet moment" for ML in science(13:20) Model explainability and transparency(17:06) Charles' Current Research(18:40) Embedding existing knowledge into ML models(22:26) "Bilingual Scientists"(24:46) Learning ML as a traditional scientist(28:22) Private vs Public ML Research(32:42) Rise of open-access research(35:22) "SOTA chasing" in ML research(38:10) Scientific ML research processes(44:34) Applying ML knowledge to a scientific problem(48:00) Biggest opportunities for ML in science(51:18) Diversity in the research community(54:24) Writing the ML4Sci newsletter(56:20) Keeping up with new research(01:05:30) Rapid Fire QuestionsLinks:Charles' ML4Sci newsletterCharles' article on AI-powered Science as a ServiceCharles' article on Deep Learning in ScienceCharles' article on Scientific GatekeepingCharles' article on Open Access ResearchGoogle Weather Forecasting paperGoogle 2nd Weather Forecasting paper DeepMind Protein Folding paper<a href="https://www.biorxiv.org/content/10.1101/2020.03.07.982272v1.full.pdf" rel="noopener...

Shawn Wang formerly worked in finance as a derivatives trader and equity analyst before burning out and pivoting towards tech. He's a prolific blogger who goes under the pseudonym "swyx" and recently published the excellent&nbsp;Coding Career Handbook. He's a graduate of Free Code Camp and Full Stack Academy now working at AWS as a Senior Developer Advocate. Learn more about Shawn:Blog:&nbsp;https://swyx.io/Book (Use code MLE30 for 30% off!):&nbsp;https://www.learninpublic.org/Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge:&nbsp;https://www.givingwhatwecan.org/Subscribe to ML Engineered: https://www.mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(05:30) How was the learning in public idea developed?(07:45) No zero days(10:00) Does ego prevent developers from learning in public?(12:30) Pick up what they put down(17:30) Strategic thinking about coding careers(19:50) Betting on new technologies(24:00) Enhancing existing skills vs learning new things(27:40) Reading technical books cover-to-cover(30:00) Systems thinking(32:00) Updating a digitally-native book(35:00) Deciding to work at AWS(38:00) What won't change in tech?(41:30) Software business models(43:00) Rapid fire questionsLinks:Free Code Camp interview: Leaving a $350K/year job to learn codingNo Zero DaysYou Can Learn A Lot For The Low Price Of Your EgoShawn’s book: The Coding Career HandbookLearn in PublicMarketing Yourself as a DeveloperCrossing the ChasmHow to Create LuckLaws of UXEugene Wei - Invisible AsymptotesEugene Wei - Status as a Service

Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge:&nbsp;https://www.givingwhatwecan.org/Subscribe to ML Engineered:&nbsp;https://www.mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(03:00) How did I get exposed to computer science and what made me pursue it?(10:00) Why machine learning?(15:10) How did I learn ML? How would I recommend someone do it today?(27:00) Why start this podcast? What is the goal?(29:40) Rapid-fire questionsLinks:&nbsp;Marc Andreeson: Software is eating the world&nbsp;Breaking Smart&nbsp;Andrew Ng's ML course&nbsp;deeplearning.ai&nbsp;Stanford CS224n&nbsp;Stanford CS231n&nbsp;VARK Learning Styles&nbsp;Deep Learning textbook&nbsp;FastAI Practical Deep Learning for Coders&nbsp;Naval Podcast&nbsp;swyx: Learn in Public&nbsp;Seth Godin Akimbo Podcast Workshop&nbsp;Marcus Aurelius: Meditations&nbsp;The ONE Thing

Karthik Suresh works as a software engineer at Blend, the leading digital lending platform. He previously worked at Coursera, Biomedtrics, and KloudData. Learn more about Karthik:&nbsp;http://karthiksuresh.me/Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge:&nbsp;https://www.givingwhatwecan.org/&nbsp;Subscribe to ML Engineered:&nbsp;https://www.mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(02:40) How were you exposed to CS? Why did you decide to pursue it?(10:30) What advice would you give yourself going into college?(20:00) Does GPA matter?(30:20) Job hunting in college(36:05) Working at Blend(39:00) Internships(45:50) Startups vs big tech companies(58:30) Trends in fin-tech(01:05:00) Rapid fire questionsLinks mentioned:Blend&nbsp;Nickel and Dimed&nbsp;Grant&nbsp;Last Chance U

Jordan Dunne works as a Technical Program Manager at Google Payments. He previously worked as a Program Manager at Microsoft, Lead Forward-Deployed Engineer at Enlitic, and Product Manager at Vim. Learn more about Jordan:&nbsp;https://www.linkedin.com/in/jordanwdunne/Want to level-up your skills in machine learning and software engineering? Subscribe to our newsletter: https://mlengineered.ck.page/943aa3fd46Take the Giving What We Can Pledge:&nbsp;https://www.givingwhatwecan.org/Subscribe to ML Engineered:&nbsp;https://www.mlengineered.com/listenFollow Charlie on Twitter: https://twitter.com/CharlieYouAITimestamps:(02:00) How were you exposed to CS and why did you pursue it?(03:25) Is software engineering actually engineering?(06:40) How do you define product management?(11:05) When did you realize you wanted to be a PM instead of a developer?(16:40) Project vs Program vs Product Management(18:35) Effective PM as leverage on a dev team(24:05) What can engineers do to make PM's lives easier?(26:10) Companies moving towards technical PMs?(30:20) Handling the added uncertainty from Data/ML products(42:00) ML models held to a higher standard than their human equivalents(45:15) Why are Xoogle PMs so successful?(52:10) Google's and Boeing's cultures influenced by their business models(56:00) "Needless complexity" in PM(59:00) Getting better at estimation(01:04:00) Knowing ML evaluation metrics as a PM(01:06:30) Getting better at communication(01:14:20) Prioritizing what to learn(01:16:50) Keeping the big picture in mind(01:20:00) Rapid fire questionsLinks:Thanks for the FeedbackBetter Angels of Our NatureCrucial ConversationsThe Great Escape

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