Quality Of Machine Learning Models
summary
Defines model quality by measurable outcomes not topic labels and compares metrics: recall over precision for pneumonia x rays, RMSE and R² for economic regression; shows a training run where training early stop at iteration 7 reduces cost and improves val loss; recommends dataset curation, versioning and cross‑validation, and monitors skew and drift in production; advertises tooling with Vertex AI Pipelines, TFX, TensorBoard, Model Monitoring and Model Cards to explain feature importance and catch bias.
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chapters (13)
- 0:00ML quality introduction
- 1:09Defining ML model quality
- 6:29Classification quality metrics
- 9:16Regression quality metrics
- 12:07Underfitting and overfitting
- 14:15Monitoring training and evaluation
- 18:17Testing ML model challenges
- 21:27Handling chaotic data
- 24:11Fairness and bias in predictions
- 27:44Monitoring model drift
- 29:54Model quality assurance steps
- 32:45Vertex AI quality tools
- 38:49Q&A
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