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Home/Tasks/Hopsworks
Hopsworks logo

Hopsworks

The first open-source feature store for high-performance ML pipelines.

DevelopmentAPI available
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Feature ManagementFeature Ingestion
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About Hopsworks

Hopsworks is a modular, high-performance AI platform that pioneered the concept of the Feature Store for machine learning. Designed for the 2026 enterprise landscape, it provides a centralized repository for managing, discovering, and serving features to both online and offline models. Its technical architecture is built on top of RonDB, an ultra-low-latency in-memory database, which enables it to serve online feature vectors in sub-millisecond timeframes. Hopsworks bridges the gap between data engineering and data science by offering a unified API (HSFS) for feature ingestion, transformation, and retrieval. By providing strong data consistency and point-in-time joins, it prevents data leakage—a critical requirement for training accurate financial and healthcare models. The platform includes a Model Registry and integrated compute capabilities using Spark and Flink, making it a comprehensive solution for managing the end-to-end ML lifecycle. In the 2026 market, Hopsworks distinguishes itself through its multi-cloud and on-premise flexibility, catering to organizations that require strict data sovereignty while demanding the scalability of a managed serverless environment.

Core Capabilities

Hopsworks is a modular, high-performance AI platform that pioneered the concept of the Feature Store for machine learning.

Main Tasks

Feature Management

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Feature Ingestion

Explore all tools that specialize in feature ingestion. This domain focus ensures Hopsworks delivers optimized results for this specific requirement.

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Online Model Serving

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Data Validation

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Model Versioning

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Decision Summary

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MLOps Platform
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API available
Web-first workflow
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Core Tasks

  • Feature Management
  • Feature Ingestion
  • Online Model Serving
  • Data Validation
  • Model Versioning

Target Personas

MLOps Platform

Categories

DevelopmentData & Ml

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