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Python
Feature Store
One source of truth for all your AI features. Build pipelines once, deploy anywhere and ensure data consistency while serving features in sub-milliseconds.
Scheduled upgrade on April 4, 08:00 UTC
Peer reviewed performance, sub-millisecond latency with RonDB, our real-time database.
Millisecond latency for end-to-end data retrieval with the best-in-class feature store.
GPU and compute management for LLMs and other ML models.
Unify your Compute and Data Lake, Data Warehouse and Databases in the industry best Feature Store.
Any frameworks and languages. Minimal ramp-up, no lock-in and easy adoption.
Any data sources and data pipelines in SQL/Spark/Flink or any Python framework.
Reduced costs
Up to 80% cost reduction by reusing features and streamlining development.
Enhanced efficiency
Achieve 10x times faster ML pipelines with our end to end integrated tools, query engine and frameworks.
Improved governance
100% audit coverage and role-based access control for airtight compliance.
Peer reviewed performance, sub-millisecond latency with RonDB, our real-time database.
Unify your Data Lake, Data Warehouse and Databases in a MLOps-ready platform.
Any cloud, hybrid, on-premises, air-gapped, powered by Kubernetes.
Millisecond latency for end-to-end data retrieval with the best-in-class feature store.
Any data sources and data pipelines in SQL/Spark/Flink or any Python library.
Reduced costs, ehanced efficiency while improving governance.
GPU and compute management in LLMs and for ML models.
Any frameworks and languages. Minimal ramp-up, no lock-in and easy adoption.
Read more about the capabilities of the Hopsworks AI Lakehouse.
Peer reviewed performance, sub-millisecond latency with RonDB, our real-time database.
Unify your Data Lake, Data Warehouse and Databases in a MLOps-ready platform.
Any cloud, hybrid, on-premises, air-gapped, powered by Kubernetes.
Millisecond latency for end-to-end data retrieval with the best-in-class feature store.
Any data sources and data pipelines in SQL/Spark/Flink or any Python library.
Reduced costs, ehanced efficiency while improving governance.
GPU and compute management in LLMs and for ML models.
Any frameworks and languages. Minimal ramp-up, no lock-in and easy adoption.
"Our journey with Hopsworks has been an amazing transformation that's really enabled us to be innovative and reach a point that we wouldn't have been able to reach otherwise.”
Introduction to Great Expectations concepts and classes which are relevant for integration with the Hopsworks MLOps platform.
Perform feature engineering in DBT on BigQuery.
This example shows the Hopsworks Python API for Kafka - manage schemas, topics and set up the connection to the kafka brokers.
Predict customers that are at risk of churning.
How to implement feature monitoring in your production pipeline.
An introductory tutorial including all the main concepts to get started easily.
Achieve an 80% reduction in cost over time starting from the second ML models are deployed in production.
MLOps with a feature store allows your organisation to put your data into production, faster.
Accelerate your machine learning projects and unlock the full potential of your data with our feature store comparison guide.
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Feature engineering at reasonable scale. Bring your own code with you, use any popular library and framework in Hopsworks.
Role-based access control, project-based multi-tenancy, custom metadata for governance.
Feature Engineering at scale, and with the freshest features. Batch or Streaming feature pipelines.
Bring Your Own Cloud, your infrastructure, on-premise or anywhere else; managed clusters on AWS, Azure, or GCP.
Use Python, Spark or Flink with the highest performance pipelines for reading and writing features.
Enterprise Support available 24/7 on your preferred communication channel. SLOs for your feature store.