Amazon SageMaker Feature Store
Fully managed repository to create, store, share, and serve ML features
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Real-time AI Lakehouse with Python-centric Feature Store
Hopsworks delivers a real-time AI lakehouse, offering a Python-focused feature store, MLOps tools, multi-tenant projects, and flexible deployment on cloud, serverless, or on-premises.
Hopsworks is designed for data science and engineering teams that need a unified platform to build, govern, and serve machine‑learning assets. It combines a Python‑centric feature store with full MLOps capabilities, enabling collaborative development across projects while maintaining strict data governance.
The platform provides project‑based multi‑tenancy, versioned feature groups, lineage tracking, and integrated tools such as Jupyter notebooks, Conda environments, Airflow pipelines, and GPU‑accelerated training. Users can run Spark, Flink, or streaming jobs and serve models through built‑in APIs or external services like Databricks, SageMaker, and Kubeflow.
Hopsworks can be consumed as a managed service on AWS, Azure, or GCP, as a serverless app at app.hopsworks.ai, or installed on‑premises using a simple installer on CentOS/RHEL 8.x or Ubuntu 22.04. The on‑premises option supports air‑gapped environments and offers full control over hardware and security policies.
When teams consider Hopsworks, these hosted platforms usually appear on the same shortlist.
Fully managed repository to create, store, share, and serve ML features
Feature registry with governance, lineage, and MLflow integration
Central hub to manage, govern, and serve ML features across batch, streaming, and real time
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Fraud detection with real-time scoring
Features are ingested and served instantly, enabling online models to flag fraudulent transactions as they occur.
Customer churn prediction batch pipeline
Batch feature pipelines generate nightly datasets, train models, and store predictions for downstream analytics.
Cross-team model governance
Projects isolate development, staging, and production, providing versioned models and audit trails for regulatory compliance.
Hybrid cloud training
Data engineers run Spark jobs on on-premises clusters while scaling GPU training in the cloud via managed Hopsworks.
CentOS/RHEL 8.x and Ubuntu 22.04 are officially supported.
Yes, the installer lets you run Hopsworks on any compatible Linux VM or bare-metal server.
It provides project-based multi-tenancy, fine-grained permissions, versioning, lineage, and provenance for all ML assets.
The serverless app at app.hopsworks.ai is publicly accessible and can be used for tutorials and small experiments.
Hopsworks integrates with Databricks, SageMaker, and Kubeflow, and offers its own MLOps APIs for model registry and deployment.
Project at a glance
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