AWS ML Engineer Associate 4.3 Secure AWS ML Resources

The final course in this domain builds on the skills to secure your AWS resources in your machine learning (ML) solution. You will implement security controls using the principle of least privilege and configure AWS Identity and Access Management (IAM) policies and roles for users and applications that interact with your ML systems. Finally, you will explore the Amazon SageMaker security and compliance features to learn how to meet your company's security requirements.

• Course level: Advanced
• Duration: 2 hours and 15 minutes

Activities

• Online materials
• Exercises
• Knowledge check questions

Course objectives

• Describe the shared responsibility model for securing ML solutions.
• Implement principle of least privilege on ML artifacts.
• Apply IAM policies and roles for users and applications that interact with ML systems.
• Configure virtual private cloud (VPC) networks for SageMaker endpoints.
• Implement network access controls to secure and isolate ML systems.
• Describe SageMaker security and compliance features.
• Use SageMaker security and compliance features to troubleshoot and debug security issues.
• Explain security best practices for continuous integration and continuous delivery (CI/CD) pipelines.

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

Section 2: Securing ML Resources

• Lesson 3: Securing AWS Resources in Your ML Solution
• Lesson 4: Shared Responsibility Model
• Lesson 5: Access Control Capabilities Using IAM
• Lesson 6: Principle of Least Privilege
• Lesson 7: Network Access Controls for ML Resources
• Lesson 8: Demo: Securing ML Resources

Section 3: Amazon SageMaker Compliance and Governance

• Lesson 9: Security and Compliance Features
• Lesson 10: Compliance and Governance Features

Section 4: Security Best Practices for CI/CD Pipelines

• Lesson 11: Security Considerations for CI/CD Pipelines

Section 5: Implement Security and Compliance through Monitoring, Auditing, and Logging

• Lesson 12: Implementing Security and Compliance through Monitoring and Logging

Section 6: Conclusion

• Lesson 13: Course Summary
• Lesson 14: Assessment
• Lesson 15: Contact Us

Keywords

• Gen AI
• Generative AI