This course covers part of the deployment phase of the machine learning (ML) lifecycle. In this course, you will learn how to move a trained model into production and manage the infrastructure that serves it. This course covers selecting deployment targets and inference strategies on Amazon SageMaker AI, including real-time, serverless, asynchronous, and batch inference, and bringing in models built outside Amazon Web Services (AWS). You will also learn to deploy generative AI workloads, including foundation model hosting and agents with Amazon Bedrock and retrieval augmented generation (RAG) systems with Amazon Bedrock Knowledge Bases.
ML Engineer - Manage Deployment Infrastructure
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ML Engineer - Manage Deployment Infrastructure
This course is part of AWS ML Engineer Associate Professional Certificate
Instructor: AWS Instructor
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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 deployment infrastructure for ML and AI workloads based on latency, cost, and scale requirements.
Differentiate appropriate compute environments and deployment targets for ML workloads.
Differentiate deployment orchestrators and multi-model or multi-container strategies.
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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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There are 5 modules in this course
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