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Effortlessly manage, store, and compute features from any data source, at any scale, for any ML use case. Fully integrated within the Hopsworks AI Lakehouse, Hopsworks Feature Store forms the unified data backbone that enables efficient feature management across any ML lifecycle.
Populate the store or leverage it as a feature catalog within your existing infrastructure, enabling seamless serving of thousands of models.
From on-demand computed features and real-time inference with millisecond freshness, to powerful batch predictions and LLM use cases, Hopsworks leads the industry in delivering advanced feature store capabilities tailored to any organisation size.
What’s in the box?
- Centralized Feature Management
Avoid duplication and inconsistency—store and manage features centrally, making them easily discoverable and reusable across teams and projects. - Real-Time and Batch Feature Serving
Serve real-time predictions with millisecond latency or handle large-scale batch processing effortlessly, all from one unified platform. - Seamless Integration into your ML Workflow
Directly integrate with popular ML frameworks and orchestration tools (Airflow, Spark, Flink, Ray, PyTorch, TensorFlow, and more). Streamline your end-to-end feature engineering and model training pipelines. - Built-in Data Quality and Governance
Ensure accuracy and reliability with automated validation, monitoring, lineage tracking, and robust access control. - Scalable and Cost-Efficient Infrastructure
Effortlessly handle petabytes of data and thousands of features, optimizing resource usage and reducing operational overhead both on-premises and in the cloud. - Observability & Control
Gain full visibility into feature usage, track feature statistics in real-time, and enforce quotas and governance policies to optimize costs and maintain compliance.
Deploy anywhere on the cloud or on-premises within a sovereign AI infrastructure.
