This course covers part of the model development phase of the machine learning (ML) lifecycle. In this course, you will learn the core mechanics of model training and how to run training jobs on Amazon SageMaker AI. This course covers how a model learns, including loss functions, optimization, and hyperparameters, and how to choose a compute environment such as CPUs, GPUs, or AWS Trainium. You will learn how to train with Amazon SageMaker AI built-in algorithms and script mode, how to reduce training time with early stopping and distributed training, and how to tune hyperparameters with automatic model tuning.
ML Engineer - Train Models
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ML Engineer - Train Models
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
Differentiate appropriate training approaches and configurations for ML models using SageMaker AI.
Define core elements in the model training process.
Explain SageMaker AI built-in algorithms and common ML libraries for training.
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Recently updated!
November 2026
Assessments
7 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 3 modules in this course
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