Evaluate the security and governance requirements for an ML use case and describe possible solutions and mitigation strategies.
Set up experimentation environments for MLOps with Amazon SageMaker.
Explain best practices for versioning and maintaining the integrity of ML model assets (data, model, and code).
Describe three options for creating a full CI/CD pipeline in an ML context.
Recall best practices for implementing automated packaging, testing and deployment. (Data/model/code).
Demonstrate how to monitor ML-based solutions.
Demonstrate how to automate an ML solution that tests, packages, and deploys a model in an automated fashion, detects performance degradation, and re-trains the model on top of newly acquired data.
Training Prerequisites
Learning Tree course 1226, AWS Technical Essentials
Learning Tree course 1222, DevOps Engineering on AWS, or equivalent experience
Practical Data Science with Amazon SageMaker course, or equivalent experience
