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