Streamlining Machine Learning Operations
summary
Walks through concrete MLOps tools and behaviors on AWS: Amazon SageMaker Notebooks and Experiments track code and trials; Autopilot auto-generates models and exports source-code Jupyter notebooks for inspection; SageMaker training supports distributed GPU jobs and hyperparameter tuning; SageMaker Debugger stops training on tensor anomalies (exploding/vanishing gradients) and saves traces; Model Monitor computes statistics, detects data drift and sends alerts; Ground Truth auto-labels then routes low-confidence items to humans; Augmented AI lets humans review low-confidence inferences; Studio preview runs in the Ohio region.
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chapters (8)
- 0:00Introduction to MLOps
- 4:31Challenges of ML at scale
- 8:57MLOps parallels with software
- 16:43Workflow orchestration tools
- 20:05SageMaker for ML lifecycle
- 26:41Deploying models at scale
- 29:55Monitoring and human review
- 34:10Summary and key takeaways
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