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Feast Feature Store

Master Feast to centralize, manage, and serve features for machine learning models in production with consistency, speed, and scalability.
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Course Duration: 10 Hours
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Feast: Streamlining Feature Management for Production ML
 
One of the biggest challenges in production machine learning is managing and serving features consistently across training and inference environments. Feature pipelines often become complex, inconsistent, and hard to scale — leading to training-serving skew and delayed deployments. Feast (Feature Store) solves this problem by acting as a centralized platform for storing, managing, and serving features to ML models in real time or batch mode.
 
This course is designed to provide you with end-to-end knowledge of feature store concepts and practical expertise in using Feast to power production-ready machine learning systems. You’ll learn to set up a Feast environment, define feature views, ingest data from multiple sources, and serve low-latency features to online models.
 
At the heart of the course is Argo Feast Feature Store — a structured methodology for building scalable feature store workflows. Argo Feast defines best practices for:
  • Designing reusable, versioned features.
  • Managing consistency between training and serving pipelines.
  • Integrating Feast with modern MLOps stacks.
  • Ensuring data quality, governance, and monitoring.
What is Argo Feast Feature Store?
Argo Feast Feature Store is a methodology that combines Feast’s capabilities with an enterprise-grade approach to feature engineering and serving. It provides a repeatable pipeline for:
  1. Ingesting features from data warehouses, streams, or APIs.
  2. Transforming and validating features.
  3. Registering features in Feast with version control.
  4. Serving features to online and offline ML environments.
  5. Monitoring feature usage and freshness.
How to Use This Course
This course is designed to take you from the basics of feature stores to enterprise-scale deployments:
  • Start with foundations – Understand the role of a feature store in MLOps.
  • Learn Feast architecture – Components, APIs, and storage backends.
  • Implement Argo Feast methodology – From ingestion to serving.
  • Work with real-world data – Use offline (data warehouse) and online (Redis, DynamoDB) stores.
  • Integrate with ML pipelines – Connect to Kubeflow, Airflow, MLflow, and cloud AI platforms.
  • Deploy and monitor – Ensure high availability, low latency, and feature freshness.
  • Build a portfolio project – Create a feature store for a real-world ML problem like fraud detection or recommendation systems.
By the end, you’ll be able to design, implement, and operate feature store systems that power scalable and reliable machine learning 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.

    • 01:20
Course Objectives Back to Top
By the end of this course, you will be able to:
 
  1. Understand the purpose and benefits of feature stores.
  2. Apply Argo Feast methodology to real-world projects.
  3. Set up Feast with online and offline stores.
  4. Create, register, and manage feature views.
  5. Ingest features from multiple data sources.
  6. Serve features to ML models in batch or real time.
  7. Ensure feature consistency between training and inference.
  8. Integrate Feast into MLOps pipelines.
  9. Monitor and govern feature usage.
  10. Prepare for feature store and MLOps certification exams.
Course Syllabus Back to Top
Course Syllabus
 
Module 1: Introduction to Feature Stores
  • Problems solved by feature stores
  • Feast vs. other feature stores
  • Argo Feast overview
Module 2: Feast Architecture
  • Core components: Feature views, registries, providers
  • Online and offline stores
Module 3: Setting Up Feast
  • Installation and configuration
  • Local and cloud deployments
Module 4: Creating Feature Views
  • Defining feature definitions in Python
  • Entity and feature view concepts
Module 5: Data Ingestion
  • Batch ingestion from warehouses (BigQuery, Snowflake)
  • Real-time ingestion from Kafka, Kinesis
Module 6: Feature Serving
  • Online serving for real-time inference
  • Offline serving for batch training
Module 7: Feature Consistency and Versioning
  • Managing training-serving skew
  • Version control for features
Module 8: Integrating Feast with MLOps Pipelines
  • Connecting to Kubeflow, Airflow, and MLflow
  • Model deployment with Feast features
Module 9: Monitoring and Governance
  • Feature freshness and quality checks
  • Usage analytics
Module 10: Capstone Project
 
  • Designing and deploying a production-grade feature store
Certification Back to Top

Upon completion, learners will receive an Industry-Recognized Certificate in Feast Feature Store & MLOps Feature Management from Uplatz. This certification validates your ability to design, implement, and operate feature store systems using Feast. It demonstrates proficiency in managing features across training and inference environments, integrating with MLOps pipelines, and ensuring data consistency. Recognized globally, it boosts your credibility for roles such as MLOps Engineer, Data Engineer, Machine Learning Engineer, and AI Infrastructure Specialist, and serves as a valuable credential for LinkedIn, resumes, and portfolios.

Career & Jobs Back to Top
Feature store expertise is becoming a key differentiator in production AI engineering. Organizations adopting MLOps and real-time AI solutions require professionals who can manage and serve features efficiently. Career opportunities include:
  • MLOps Engineer (Feature Store Specialist)
  • Data Engineer (ML Pipelines)
  • Machine Learning Engineer
  • AI Infrastructure Architect
  • Real-Time Data Platform Engineer
Industries hiring include fintech, e-commerce, gaming, healthcare, and logistics — all relying on real-time personalization, fraud detection, and predictive analytics. Salaries range from $100,000 to $165,000 annually in global markets. Senior roles often evolve into Head of MLOps or AI Platform Architect, and consultants can specialize in building enterprise feature store solutions for multiple clients.
Interview Questions Back to Top
1. What is a feature store in machine learning?
A centralized system for storing, managing, and serving features to ML models for training and inference.
 
2. How does Feast differ from other feature stores?
Feast is open-source, lightweight, and integrates easily with cloud and on-premises pipelines.
 
3. What problem does a feature store solve?
It eliminates training-serving skew and ensures consistent, reusable features.
 
4. What are feature views in Feast?
Logical groupings of features associated with an entity, defined in Python.
 
5. What’s the difference between online and offline stores in Feast?
Online stores serve features for real-time inference; offline stores provide features for training.
 
6. How does Argo Feast methodology help?
It provides a structured approach for ingestion, versioning, governance, and deployment.
 
7. How do you integrate Feast with an MLOps pipeline?
By connecting it to orchestration tools like Kubeflow or Airflow and tracking models with MLflow.
 
8. How do you ensure feature freshness in Feast?
By monitoring ingestion pipelines and setting TTLs (time-to-live) on features.
 
9. Can Feast be deployed in the cloud?
Yes, it supports AWS, GCP, Azure, and hybrid environments.
 
10. Give an example of a real-world Feast use case.
Serving real-time user profile features for a recommendation engine in an e-commerce platform.
Course Quiz Back to Top
Start Quiz
Q1. What are the payment options?
A1. We have multiple payment options: 1) Book your course on our webiste by clicking on Buy this course button on top right of this course page 2) Pay via Invoice using any credit or debit card 3) Pay to our UK or India bank account 4) If your HR or employer is making the payment, then we can send them an invoice to pay.

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A2. Yes, you will receive course completion certificate from Uplatz confirming that you have completed this course with Uplatz. Once you complete your learning please submit this for to request for your certificate https://training.uplatz.com/certificate-request.php

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A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

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While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

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BUY THIS COURSE (USD 17 USD 41)