This course covers part of the model development phase of the machine learning (ML) lifecycle. In this course, you will learn how to choose the modeling approach that best solves a business problem. This course covers the layers of the Amazon Web Services (AWS) AI and ML stack and the tradeoffs among building a custom model, using a managed service, and adopting a pre-trained or foundation model (FM). You will learn how to evaluate and select foundation models with Amazon Bedrock, when to use managed AWS AI services such as Amazon Textract and Amazon Transcribe, and how to apply retrieval augmented generation (RAG) architecture patterns.
ML Engineer - Choose Modeling Approaches
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ML Engineer - Choose Modeling Approaches
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 modeling approaches for a given business problem.
Differentiate FMs from Amazon Bedrock based on task requirements and cost.
Identify fine-tuning strategies for pre-trained FMs and when to use each.
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 3 modules in this course
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