This course covers part of the monitoring phase of the machine learning (ML) lifecycle. In this course, you will learn how to optimize your infrastructure for performance while controlling costs. This course covers right-sizing inference with Amazon SageMaker AI, building dashboards and troubleshooting with Amazon CloudWatch, and analyzing spend with Amazon Web Services (AWS) cost management tools such as AWS Cost Explorer and AWS Budgets. You will compare purchasing options and Amazon EC2 capacity choices. For generative AI, you will manage foundation model costs through token optimization, prompt caching, and provisioned throughput with Amazon Bedrock.
ML Engineer - Optimize Costs and Performance
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ML Engineer - Optimize Costs and Performance
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 cost and performance tradeoffs for production ML and AI workloads.
Differentiate inference instance families to optimize performance and cost.
Describe troubleshooting and analysis tools for ML and AI workloads.
Details to know
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Recently updated!
November 2026
Assessments
11 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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- Develop job-relevant skills with hands-on projects
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There are 4 modules in this course
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