Course Overview
TOPActivities This course includes presentations, hands-on labs, demonstrations, knowledge checks, and workbook activities.
Scheduled Classes
TOP| 05/05/26 - TDV - Virtual-Instructor Led - Virtual-Instructor Led | (click to enroll)
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What You'll Learn
TOPExplain 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 on top of newly acquired data
- In this course, you will learn to:
Outline
TOPCourse introduction
- Machine learning operations
- Goals of MLOps
- Communication
- From DevOps to MLOps
- ML workflow
- Scope
- MLOps view of ML workflow
- MLOps cases
- Intro to build, train, and evaluate machine learning models
- Lab: Code and serve your ML model with AWS CodeBuild
- Activity: MLOps Action Plan Workbook
- MLOps security
- Automating
- Apache Airflow
- Kubernetes integration for MLOps
- Amazon SageMaker for MLOps
- Lab: Bring your own algorithm to an MLOps pipeline
- Demonstration: Amazon SageMaker
- Intro to build, train, and evaluate machine learning models
- Introduction to deployment operations
- Model packaging
- Inference
- Lab: Deploy your model to production
- SageMaker production variants
- Deployment strategies
- Deploying to the edge
- Lab: Conduct A/B testing
- Activity: MLOps Action Plan Workbook
- Lab: Troubleshoot your pipeline
- The importance of monitoring
- Monitoring by design
- Lab: Monitor your ML model
- Human-in-the-loop
- Amazon SageMaker Model Monitor
- Demonstration: Amazon SageMaker Pipelines, Model Monitor, model registry, and Feature Store
- Solving the Problem(s)
- Activity: MLOps Action Plan Workbook
- Course review
- Activity: MLOps Action Plan Workbook
- Wrap-up
Prerequisites
TOPAWS Technical Essentials (classroom or digital)
- DevOps Engineering on AWS, or equivalent experience
- Practical Data Science with Amazon SageMaker, or equivalent experience
- Recommended previous knowledge
Who Should Attend
TOPMLOps engineers who want to productionize and monitor ML models in the AWS cloud DevOps engineers who will be responsible for successfully deploying and maintaining ML models in production
