This course covers part of the deployment phase of the machine learning (ML) lifecycle. In this course, you will be introduced to machine learning operations (MLOps), which applies DevOps practices such as continuous integration and continuous delivery (CI/CD) to ML workflows. This course covers Amazon Web Services (AWS) developer tools such as AWS CodePipeline, orchestration with AWS Step Functions and Amazon SageMaker Pipelines, and event-driven retraining with Amazon EventBridge. You will also automate generative AI workflows, including prompt versioning, agent deployment, and retrieval augmented generation (RAG) updates with Amazon Bedrock.
ML Engineer - Automated Orchestration and CI/CD
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ML Engineer - Automated Orchestration and CI/CD
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
Differentiate appropriate CI/CD pipeline components for automating ML and AI workloads.
Describe automated deployment strategies with rollback actions.
Explain AWS developer tools for CI/CD and their troubleshooting approaches.
Details to know
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Recently updated!
November 2026
Assessments
12 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.
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- 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 5 modules in this course
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