What will you learn in Feature Engineering Course
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Understand how to use Vertex AI Feature Store to build, manage, and serve ML features.
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Prepare and transform raw data into ML-ready features using BigQuery ML, Keras, TensorFlow, Dataflow, and Dataprep.
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Learn about feature transformations like feature crosses, bucketing, and using tf.Transform.
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Explore best practices in preprocessing, feature exploration, and enhancing model accuracy.
Program Overview
Module 1: Introduction to Vertex AI Feature Store
~0.8 hours
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Topics: Overview of what a feature store is, why it’s essential, and its core components.
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Hands-on: Watch 6 videos + 1 reading + 1 quiz to learn setup, terminology, and purpose.
Module 2: Raw Data to Features
~1 hour
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Topics: Identify usable raw data, define feature quality, and establish feature selection criteria.
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Hands-on: Review 1 reading + 1 assignment focused on deriving features from raw datasets.
Module 3: Feature Engineering Basics
~4 hours
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Topics: Contrast ML vs statistics approaches, apply feature transformations in BigQuery ML & Keras, and use crosses, bucketing, and tf.Transform.
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Hands-on: Complete labs using BigQuery ML and TensorFlow with practical examples (e.g., housing prices, taxi fares).
Module 4: Advanced Feature Engineering & MLOps
~2 hours
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Topics: Learn advanced transformations, metadata handling, and integration with ML pipelines (MLOps).
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Hands-on: Apply tf.Transform in TensorFlow workflows and integrate features into production pipelines.
Module 5: Course Conclusion
~0.5 hours
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Topics: Summarize feature engineering best practices, review tools and production integration strategies.
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Hands-on: Complete final quizzes and reflect on end-to-end feature design.
Get certificate
Job Outlook
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Highly relevant for roles like ML Engineer, MLOps Engineer, and Data Scientist focusing on production-grade ML systems.
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Teaches one of the essential skills—feature engineering—widely recognized in roles across data-driven companies.
