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Feast Blog

26 articles total

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Feast is an end-to-end open source feature store for machine learning. It allows teams to define, manage, discover, and serve features.

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  •  Feast Gets Native Apache Iceberg Support
  •  How to Use Feast for SLM/LLM Post-Training with Ray
  •  Using Feast's OpenAI Compatible Search API
  •  Data Quality Monitoring in Feast 0.64
  •  Extending Feast Observability: Offline Store Metrics and SOX Audit Logging
  •  Tuning the Feast Feature Server for Sub-2ms Online Serving
  •  Native MLflow Integration for Feast: Automatic Feature Lineage for Every Experiment
  •  Native MongoDB Support in Feast: One Database for Operational Data, Features, and Vectors
  •  Building AI Agents with Feast: Feature Stores as Context and Memory
  •  Feast Introduces Experimental Feature View Versioning
  •  Monitoring Your Feast Feature Server with Prometheus and Grafana
  •  Feast Meets Oracle: Unlocking Feature Store for Oracle Database Users
  •  Feast + MLflow + Kubeflow: A Unified AI/ML Lifecycle
  •  Feature Server High-Availability and Auto-Scaling on Kubernetes
  •  Historical Features Without Entity IDs
  •  Tracking Feature Lineage with OpenLineage
  •  Streamlining AI Feature Engineering with Feast and dbt
  •  Feast Joins the PyTorch Ecosystem
  •  Scaling ML with Feast and Ray: Distributed Processing for Modern AI Applications
  •  From Raw Data to Model Serving: A Blueprint for the AI/ML Lifecycle with Kubeflow and Feast