This course covers part of the operations phase of the machine learning (ML) lifecycle. In this course, you will learn how to secure the Amazon Web Services (AWS) resources in your ML and artificial intelligence (AI) solutions. This course covers least-privilege access with AWS Identity and Access Management (IAM), network isolation with Amazon Virtual Private Cloud (Amazon VPC), and monitoring, auditing, and compliance with Amazon CloudWatch and AWS CloudTrail. You will also secure generative AI, including defending against prompt injection, managing foundation model credentials, and applying Amazon Bedrock Guardrails.
ML Engineer - Secure ML and AI Workloads
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ML Engineer - Secure ML and AI Workloads
This course is part of AWS ML Engineer Associate Professional Certificate
Instructor: AWS Instructor
Included with Learn more
Gain insight into a topic and learn the fundamentals.
Advanced level
Recommended experience
2 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Diagnose security risks in an ML and AI system and the appropriate controls to mitigate them.
Explain how to secure CI/CD pipelines by checking for code and image vulnerabilities.
Describe least privilege access patterns for ML and AI artifacts.
Details to know
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Recently updated!
November 2026
Assessments
10 assignments
Taught in English
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This course is part of the AWS ML Engineer Associate Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate from Amazon Web Services
There are 4 modules in this course
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