This course covers part of the data preparation phase of the machine learning (ML) lifecycle. In this course, you will learn about data transformation. This course covers various transformation concepts and techniques, such as data cleaning, categorical encoding, and feature engineering. You will discover how to use Amazon Web Services (AWS) services such as Amazon SageMaker Feature Store, Amazon SageMaker Data Wrangler, and AWS Glue to transform your data. You will also prepare data for generative AI, including configuring embedding models with Amazon Bedrock, preparing documents for retrieval augmented generation (RAG), and masking sensitive data with Amazon Macie.
ML Engineer- Data Transformation and Feature Engineering
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ML Engineer- Data Transformation and Feature Engineering
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 data transformation, feature engineering, and GenAI data preparation methods for a given dataset and modeling approach.
Describe document preparation for RAG applications including chunking strategies and metadata extraction.
Describe fundamental encoding techniques.
Details to know
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Recently updated!
November 2026
Assessments
15 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 7 modules in this course
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