This course covers part of the data preparation phase of the machine learning (ML) lifecycle. In this course, you will learn how to validate data quality and manage bias before training. This course covers data cleaning techniques for resolving incorrect, duplicated, outlier, and missing values, along with data labeling and annotation. You will validate quality with Amazon Web Services (AWS) tools such as AWS Glue Data Quality and AWS Glue DataBrew, and screen content with Amazon Comprehend and Amazon Bedrock Guardrails. You will also learn to identify and mitigate bias and to resolve class imbalance across numeric, text, and image data.
ML Engineer - Validate Data and Prepare for Modeling
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ML Engineer - Validate Data and Prepare for Modeling
This course is part of AWS ML Engineer Associate Professional Certificate
Instructor: AWS Instructor
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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 data quality and bias risks across data modalities and appropriate mitigation strategies for ML and GenAI training data.
Explain data quality validation using AWS data quality tools.
Describe data labeling and annotation using AWS services.
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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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There are 7 modules in this course
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