Overview
This course explores the benefits of using Vertex AI Feature Store, how to improve the accuracy of ML models, and how to identify which data columns make the most useful features.
The curriculum includes both theoretical content and hands-on labs focused on feature engineering using BigQuery ML, Keras, and TensorFlow.
Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
Skills Covered
Upon completion of this course, learners will be able to:
- Explain the benefits of using Vertex AI Feature Store.
- Apply feature engineering techniques to improve ML model accuracy.
- Determine which data columns are most effective as features.
- Perform feature engineering using BigQuery ML.
- Implement feature engineering workflows with Keras and TensorFlow.
Prerequisites
- Basic understanding of Machine Learning concepts
- Familiarity with SQL (for BigQuery ML) and Python (for Keras/TensorFlow)
Target Audience
- Data Scientists
- Data Analysts looking to transition into ML roles
- Aspiring or practicing Machine Learning Engineers
Course Curriculum
Module 1: Introduction to Feature Engineering
- What are features?
- Why features impact model accuracy
- Raw data vs. useful features
Module 2: Vertex AI Feature Store
- Benefits of Feature Store
- Managing, sharing, and reusing features
- Online vs. offline serving
Module 3: Feature Engineering with BigQuery ML
- Creating features from SQL queries
- Transforming data at scale
- Lab: Feature engineering in BigQuery
Module 4: Feature Engineering with Keras
- Preprocessing layers
- String lookup, discretization, normalization
- Lab: Keras feature columns
Module 5: Feature Engineering with TensorFlow
- tf.data for feature pipelines
- Feature crosses and embeddings
- Lab: TensorFlow feature engineering
Module 6: Challenge Lab (Skills Badge)
- Jump directly to a challenge lab
- Demonstrate skills without completing all modules
Dates & Locations
October 5, 2026
December 7, 2026







