Build, train, and deploy ML models using Amazon SageMaker
summary
Covers Amazon SageMaker's end-to-end ML capabilities — including labeling, data preparation, feature engineering, bias detection, training, hyperparameter tuning, model monitoring, and workflow orchestration — and shows how those built-in tools integrate to build, train, and deploy models on AWS. Also explains how to get started quickly with SageMaker.
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chapters (15)
- 0:00Intro
- 1:10Amazon SageMaker overview
- 2:37Model building and training
- 2:52SageMaker Ground Truth
- 4:5280% of time spent on data preparation
- 9:37SageMaker Processing
- 11:17SageMaker Feature Store
- 13:27SageMaker Studio notebooks
- 15:07Training algorithms and frameworks
- 18:25SageMaker training
- 21:17SageMaker managed deployments
- 25:39SageMaker multi-model endpoints
- 26:40SageMaker Model Monitor
- 28:44SageMaker Pipelines
- 30:45Learn ML with AWS Training and Certification
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