This introductory course sets the foundation for the machine learning (ML) lifecycle and role-based training for ML engineers on Amazon Web Services (AWS). In this course, you will review ML basics and the algorithms behind common solutions, and examine how ML and artificial intelligence (AI) evolved into deep learning and generative AI. You will learn how to apply ML and AI responsibly, how to formulate an ML problem from a business goal, and how to determine whether an ML solution is feasible. You will also be introduced to Amazon SageMaker AI, a fully managed service for building, training, and deploying ML models.
AWS ML Engineer Associate Curriculum Overview (ML Engineer)
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AWS ML Engineer Associate Curriculum Overview (ML Engineer)
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
1 hour to complete
Flexible schedule
Learn at your own pace
What you'll learn
Explain how foundational machine learning and AI concepts apply to formulating and scoping a feasible ML solution on AWS.
Describe key machine learning components, including ML algorithms and models.
Describe key ML capabilities and algorithms that help solve common business problems.
Details to know
Shareable certificate
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Recently updated!
November 2026
Assessments
4 assignments
Taught in English
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Build your Machine Learning expertise
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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