Feature Stores
Learn how Feature Stores streamline ML workflows by centralizing, managing, and serving features for production-ready models.Preview Feature Stores course
Price Match Guarantee Full Lifetime Access Access on any Device Technical Support Secure Checkout   Course Completion Certificate96% Started a new career BUY THIS COURSE (
USD 17 USD 41 )-
86% Got a pay increase and promotion
Students also bought -
-
- Feature Store Engineering using Feast & Tecton
- 10 Hours
- USD 17
- 10 Learners
-
- Machine Learning (basic to advanced)
- 65 Hours
- USD 17
- 4543 Learners
-
- Machine Learning with Python
- 25 Hours
- USD 17
- 3518 Learners

-
Strong understanding of how feature stores improve ML lifecycle
-
Practical experience with tools like Feast and AWS SageMaker Feature Store
-
Ability to create, store, and retrieve features for ML training and inference
-
Knowledge of architectural patterns and deployment strategies for feature stores
-
Data Scientists wanting consistency across training and production
-
ML Engineers aiming to scale model deployment
-
Data Engineers building reusable and reliable data pipelines
-
MLOps professionals automating the ML workflow
-
Anyone curious about operationalizing machine learning features
-
Start with foundational concepts to understand the problem feature stores solve
-
Practice creating and serving features using real datasets
-
Use lab environments or cloud credits to explore tools like Feast
-
Connect your learnings with broader MLOps and data engineering concepts
-
Review architecture diagrams and case studies shared in the course
-
Revisit modules as you begin applying concepts in your own ML workflows
Course/Topic 1 - Coming Soon
-
The videos for this course are being recorded freshly and should be available in a few days. Please contact info@uplatz.com to know the exact date of the release of this course.
By the end of this course, you will be able to:
-
Understand the role of Feature Stores in ML pipelines
-
Build and deploy a basic feature store using tools like Feast
-
Engineer, store, and serve features for training and inference
-
Ensure feature consistency and governance
-
Integrate feature stores with batch and streaming data sources
-
Use metadata and monitoring tools to manage feature quality
Course Syllabus
Module 1: Introduction to Feature Stores
-
What are Feature Stores?
-
Challenges in feature engineering without a store
-
Key components: feature repo, registry, online/offline stores
Module 2: Architecture and Design Patterns
-
Online vs. Offline feature stores
-
Feature groups, feature views, and entities
-
Real-world architecture examples
Module 3: Hands-on with Feast
-
Installing and configuring Feast
-
Creating a feature repository
-
Storing and retrieving features
Module 4: Batch and Streaming Ingestion
-
Batch ingestion from warehouses (BigQuery, Redshift)
-
Real-time ingestion with Kafka and Spark
Module 5: Feature Serving for Inference
-
Online serving and latency concerns
-
Integration with model prediction services
-
Versioning and consistency
Module 6: Metadata and Governance
-
Managing feature metadata and documentation
-
Access control and data lineage
-
Feature lifecycle management
Module 7: Case Studies and Best Practices
-
How top companies use Feature Stores
-
Feature reuse across teams
-
Scaling Feature Stores in enterprise environments
Module 8: Final Project
-
Build your own feature store
-
Serve features for a sample ML model
-
Document your infrastructure setup and deploy
This course prepares you for cutting-edge roles such as:
-
MLOps Engineer
-
ML Infrastructure Engineer
-
Machine Learning Engineer
-
Data Platform Engineer
-
Feature Engineering Specialist
-
What is a Feature Store and why is it important in machine learning?
-
How does a Feature Store maintain consistency between training and inference?
-
What are the differences between online and offline feature stores?
-
How does Feast work, and what are its core components?
-
What are Feature Views in Feast, and how are they defined?
-
How can streaming data be integrated into a feature store?
-
What are some challenges in serving real-time features?
-
How do you ensure feature quality and governance?
-
What is the role of metadata in a Feature Store?
-
How can a Feature Store improve collaboration between data scientists and data engineers?