MLOps Engineering on AWS
What Will You Learn?
- In this course, you will learn how to:
- Explain the benefits of MLOps.
- Compare and contrast DevOps and MLOps.
- 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