This course covers part of the deployment phase of the machine learning (ML) lifecycle. In this course, you will learn how to build and script the infrastructure that runs ML solutions, with a focus on maintainability, scalability, and cost efficiency. This course covers infrastructure as code with Amazon Web Services (AWS) CloudFormation and the AWS Cloud Development Kit (AWS CDK), programmatic deployment with the Amazon SageMaker Python SDK, virtual private cloud (VPC) configuration, and auto scaling. You will also configure knowledge bases, retrieval pipelines, agent state, and GPU scaling for generative AI workloads with Amazon Bedrock.
ML Engineer - Provision and Configure Resources
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ML Engineer - Provision and Configure Resources
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 compute, storage, networking, and AI-specific resource configurations for ML and AI workloads.
Explain resource provisioning tradeoffs between on-demand and provisioned resources.
Describe compute provisioning automation.
Details to know
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Recently updated!
November 2026
Assessments
14 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
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There are 5 modules in this course
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