52 Weeks of Cloud Episode 28: Conversation with Piero Molino and Ludwig/Predibase
Detailed conversation about Declarative AutoML with Piero Molino author of Ludwig and co-founder Predibase. 00:00 Intro 00:50 First meeting Piero at Strata in Moscone Center 10:50 Why Declarative AutoML? 12:00 Introducing Declarative ML Systems 16:44 Ludwig: declarative ML systems on PyTorch 18:06 Github Statistics for Ludwig 19:51 Ludwig training example via CLI 21:09 pip install ludwig and using the Programmatic API 26:32 How does Ludwig work? 26:53 Training with Ludwig 27:34 Predicting with Ludwig 29:59 Running Experiments with Ludwig 31:04 ludwig experiment --dataset 32:09 Input - Encoder - Decoder - Output 34:15 ParallelCNN encoding 35:42 Pretrained Transformers: bert distilbert t5 roberta gpt-2 40:44 concat combiner 41:18 Number features decoding 41:37 Vector featuers decoding 41:52 Sequence features decoding 42:30 Training parameters: batch_size, epochs, learning_rate, ... 43:07 Preprocessing parameters 43:33 Speaker Verification 44:03 Expected Time of Delivery 44:35 Summarization 45:04 Distributed Training, Ludwig on Ray 45:23 Running Ludwig on Ray 47:18 Ludwig Hyperpot with RayTune (Advanced) 48:51 Ludwig on Kubernetes 49:15 Managed Ludwig in Predibase 49:54 Predibase: Low-code ML, High-Performance
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Show Notes
If you enjoyed this video, here are additional resources to look at:
Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale
Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke
AWS Certified Solutions Architect - Professional (SAP-C01) Cert Prep: 1 Design for Organizational Complexity:
https://www.linkedin.com/learning/aws-certified-solutions-architect-professional-sap-c01-cert-prep-1-design-for-organizational-complexity/design-for-organizational-complexity?autoplay=true
O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017
O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/
Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/
Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ
Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8
Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7
Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7
Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q
Subscribe to 52 Weeks of AWS Podcast: https://52-weeks-of-cloud.simplecast.com
View content on noahgift.com: https://noahgift.com/
View content on Pragmatic AI Labs Website: https://paiml.com/
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