AWS ML Engineer Associate 3.3 Automate Deployment

This course introduces MLOps, a methodology that applies DevOps practices, like continuous integration and continuous delivery (CI/CD), to ML workflows. You will learn about creating continuous flow structures to invoke pipelines. You will also make critical decisions about deployment infrastructure and automation strategies, focusing on risk mitigation and performance enhancement in production environments. Finally, this course covers building and integrating mechanisms to retrain models, which is essential for accommodating new data or updates to model code.

• Course level: Advanced
• Duration: 1 hour 15 minutes

Activities

• Online materials
• Knowledge check questions
• A course assessment

Course objectives

• Describe DevOps.
• Describe the software release process.
• Describe how CI/CD principles fit into ML workflows.
• Describe MLOps, teams involved, requirements.
• Identify requirements for DevOps for ML.
• Describe the benefits of automating testing in CI/CD pipelines.
• Describe how Amazon SageMaker Projects brings CI/CD practices to ML.
• Describe version control systems and basic usage.
• Create continuous flow structures to invoke pipelines.
• Describe, configure, and troubleshoot AWS CodePipeline, AWS CodeCommit, AWS CodeBuild, and AWS CodeDeploy.
• Describe how data ingestion is automated and integrated with ML pipeline orchestration services.
• Automate ML workflows with AWS Step Functions and AWS CodePipeline.
• Define deployment strategies and rollback actions.
• Describe how code repositories work when a pipeline is invoked.
• Explain how to integrate ML models into a production environment.
• Build and integrate mechanisms to retrain models.
• Configure inferencing jobs.

Intended audience

• Cloud architects
• Machine learning engineers

Recommended Skills

• Completed at least 1 year of experience using SageMaker and other AWS services for ML engineering
• Completed at least 1 year of experience in a related role, such as backend software developer, DevOps developer, data engineer, or data scientist
• A fundamental understanding of programming languages, such as Python
• Completed preceding courses in the AWS ML Engineer Associate Learning Plan

Course outline

• Section 1: Introduction
  • Lesson 1: How to Use This Course
  • Lesson 2: Course Overview
  • Lesson 3: Introduction to DevOps
• Section 2: Continuous Integration and Continuous Deployment
  • Lesson 4: Introduction to MLOps
  • Lesson 5: Automating Testing in CI/CD Pipelines
  • Lesson 6: Version Control Systems
  • Lesson 7: Continuous Flow Structures
• Section 3: AWS Software Release Process
  • Lesson 8: Continuous Delivery Services
  • Lesson 9: Best Practices for Configuring and Troubleshooting
  • Lesson 10: Automating Data Integration in ML Pipeline
• Section 4: Automating Orchestration
  • Lesson 11: AWS Step Functions and AWS CodePipeline
  • Lesson 12: Deployment Strategies
• Section 5: Retraining Models
  • Lesson 13: How Code Repositories Work
  • Lesson 14: Building and Integrating Mechanisms for Retraining
  • Lesson 15: Configuring Inferencing Jobs
• Section 6: Conclusion
  • Lesson 16: Course Summary
  • Lesson 17: Assessment
  • Lesson 18: Contact Us