Implementing MLOps practices on AWS
summary
Shows how to use Amazon SageMaker to apply MLOps patterns across the ML lifecycle: provisioning reproducible development environments, automating workflows and CI/CD for model training and deployment, monitoring models in production, and enforcing standardized governance. Covers specific SageMaker features that support these capabilities for teams of data scientists and ops engineers.
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chapters (9)
- 0:00Start
- 7:28Amazon SageMaker Projects - High Level
- 12:52Built-In ML-Ops Project Templates
- 23:50Pain Points
- 25:52Measure
- 26:21SageMaker and Friends
- 27:50SageMaker Pipeline
- 30:20Results -Speed of Innovation
- 34:13Takeaways & Next Steps
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