A Chat with Andrew on MLOps: From Model-centric to Data-centric AI

A Chat with Andrew on MLOps: From Model-centric to Data-centric AI

Mar 24, 2021
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In this event, Dr.Andrew Ng shared the skills he sees as fundamental to the next generation of machine learning practitioners and followed with a Q&A.

1:23 AI Systems = Code + Data 2:16 Inspecting steel sheets for data 3:35 Should the team improve the code or data? 5:23 Improving the code vs data 6:52 Data is Food for AI 9:43 Lifecycle of an ML Project 11:11 Scoping: Speech Recognition 11:23 Collect Data: Speech Recognition 13:03 Iguana Detection Example 15:16 Making data quality systemic: MLOps 16:25 Labeler consistency example 17:27 Making it systemic: MLOps 19:52 Think about the last supervised learning model you trained. How many training examples did you have? 22:33 Kaggle Dataset Size 24:09 Small Data and Label Consistency 26:40 Theory: Clean vs noisy data 29:11 Example: Clean vs noisy data 32:12 Train model: Speech Recognition 35:53 Deploy: Speech Recognition 37:06 Making it systemic: The rise of MLOps 38:49 Traditional software vs AI software 40:12 MLOps: Ensuring consistency high quality data 41:43 Who do you think is best qualified to take on an MLOps role? 43:45 From Big Data to Good Data 45:54 Takeaways: Data-centric AI 48:52 Questions
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