Productionize with Kubeflow Orchestration and Feast in GCP - Workshop
Develop a machine learning framework using Kubeflow, Feast, and Kafka in GCP. Learn to manage data features, serve endpoints, and monitor model performance with Grafana for real-time and batch predictions.
An AI Engineer Technical Guide to Feature Store with FEAST
Comprehensive guide to FEAST feature store for AI engineers, covering installation, setup, data handling, and integration with machine learning models. Includes hands-on examples and architecture review.
Scaling Data and ML with Apache Spark and Feast - Feature Engineering for Production
Explore scaling data and ML with Apache Spark and Feast, focusing on feature engineering challenges and solutions for big data-driven machine learning in production environments.
Self-serve Feature Engineering Platform Using Flyte and Feast
Explore a self-serve feature engineering platform leveraging Flyte and Feast technologies, enhancing data science workflows and machine learning model development.
Integrating Feast Online Feature Store with KFServing
Explore integrating Feast online feature store with KFServing for efficient machine learning model serving, featuring insights from IBM experts on deployment and implementation.
Integrating High Performance Feature Stores with KServe Model Serving
Explore high-performance feature stores integration with KServe for efficient ML model serving, focusing on Feast concepts, KServe challenges, and scalable architectures.
The Challenges of Deploying Real-time AI for Finance and How Open Source Can Help
Exploring challenges in deploying real-time AI for finance and how open-source solutions like feature stores can address them. Includes case studies and resources for implementing MLOps in fintech.
Building a Cloud Native Feature Store with Feast on Kubeflow
Explore how Feast, an open-source feature store, solves key challenges in machine learning feature engineering, from data processing to production deployment, within a cloud-native Kubeflow environment.
Building a Fraud Detection Model with Feature Stores - Includes Shopify Case Study
Develop a real-time fraud detection system using ML data pipeline tools. Learn to build, deploy, and serve data pipelines with Feast and Tecton. Explore architectural patterns and model stages.
AI Innovations: The Power of Feature Platforms - MLOps Mini Summit
Explore feature platforms for production AI, including Tecton's unified approach, Cleo's custom solution, and DIY strategies for building scalable, flexible ML infrastructures.