Train and tune ML models to the highest accuracy using Amazon SageMaker
summary
SageMaker Studio shows concrete workflows: notebooks start in seconds, training jobs run on managed EC2 instances (including spot instances reduce cost), and you can bring scripts or custom Docker images. Autopilot auto-generates notebooks, trains 250 candidate models by default and produced an example RMSE 69 aggregated to weekly <1% error for shipment-date regression. Debugger captures tensors for visualization, and the built-in Bayesian hyperparameter tuner orchestrates parallel child jobs and selects the best candidate for one‑click deployment.
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chapters (11)
- 0:00Introduction
- 0:15Session agenda
- 0:45SageMaker Studio overview
- 2:02Studio features and setup
- 5:25Cloud vs local development
- 9:21Training algorithms
- 12:52Training job workflow
- 16:14Cost optimization and tuning
- 18:07SageMaker Autopilot
- 21:27SageMaker Debugger
- 23:55Autopilot demo
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