AWS ML Engineer Associate 3.2 Create and Script Infrastructure

The second course in this domain examines building and scripting infrastructure for machine learning (ML) solutions, with a focus on maintainability, scalability, and cost-efficiency. You will explore infrastructure as code (IaC) to programmatically create, deploy, and manage infrastructure. Additionally, you will learn to use tools like the AWS Cloud Development Kit (AWS CDK) to define cloud infrastructure in code and automate provisioning through AWS CloudFormation. This course also covers the Amazon SageMaker Python SDK for deploying ML models in the AWS Cloud. You will also learn about using containers to support your DevOps efforts and about Amazon Web Services (AWS) services for managing and hosting containers. Finally, you will examine and explore auto scaling methods for optimal application performance.

• Course level: Advanced
• Duration: 1 hour 30 minutes

Activities

• Online materials
• Exercises
• Knowledge check questions

Course objectives

• Describe best practices for creating ML solutions that are maintainable, scalable, and cost efficient.
• Describe the benefits and use cases of IaC.
• Describe CloudFormation as a method for automatically provisioning resources.
• Describe AWS CDK as a method for automatically provisioning resources.
• Describe examples of deploying resources with code for AWS CDK and CloudFormation.
• Compare and contrast AWS CDK and CloudFormation for automating resources.
• Describe how to build and maintain containers.
• Explain containerization concepts and AWS container services.
• Deploy and host ML models with the Amazon SageMaker SDK.
• Choose metrics for auto scaling deployments based on key benefits.
• Describe how to meet scalability requirements with SageMaker endpoint auto scaling policies.

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: AWS Machine Learning Deployment Best Practices
• Section 2: Methods for Provisioning Resources
  • Lesson 4: Infrastructure as Code
  • Lesson 5: Working with AWS CloudFormation
  • Lesson 6: Working with the AWS CDK
  • Lesson 7: Comparing AWS CloudFormation and AWS CDK
• Section 3: Deploying and Hosting Models
  • Lesson 8: Amazon SageMaker Python SDK
  • Lesson 9: Building and Maintaining Containers
  • Lesson 10: Autoscaling Inference Infrastructure
• Section 4: Conclusion
  • Lesson 11: Course Summary
  • Lesson 12: Assessment
  • Lesson 13: Contact Us

Keywords

• Gen AI
• Generative AI