Build, train and deploy Machine Learning models with Amazon SageMaker
summary
Amazon SageMaker tools for building, training, and deploying machine learning models are presented, including notebooks, experiment management, automatic model creation, model debugging, and model monitoring. The talk explains using these integrated development environment (IDE) features to speed production deployment, reduce effort, and lower costs.
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chapters (15)
- 0:00Start
- 0:20Agenda
- 0:44AWS service stack for ML
- 3:18Amazon SageMaker fundamentals
- 7:51Amazon SageMaker Studio
- 10:43Amazon SageMaker Notebook Instances
- 11:44Amazon SageMaker training clusters
- 14:05Create a RESTful API around the model
- 16:14Develop a multi-account strategy
- 17:21In 2019 Amazon SageMaker launched
- 18:54Amazon SageMaker Autopilot
- 19:41Ship date estimation
- 21:30SageMaker Debugger: Class activation maps
- 21:56Creating a multi-model endpoint
- 22:37Amazon SageMaker Model Monitor
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