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.
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.
In this example, you write a PySpark program that produces and consumes messages to/from a Kafka cluster. This program can only be run from inside Hopsworks.
Introduction to Great Expectations concepts and classes which are relevant for integration with the Hopsworks MLOps platform.
Predict the fare amount for a taxi ride in New York City given the pickup and drop off locations.
How to register Sklearn Transformation Functions and Keras model in the Hopsworks Model Registry, how to retrieve them and then use in training and inference pipelines.
Predict Bitcoin price using time series features and tweets sentiment analysis.
This example shows the Hopsworks Python API for Kafka - manage schemas, topics and set up the connection to the kafka brokers.
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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.
Dive in our documentation and start using the Feature Store right away.
For fast-moving development cycles and product launches, Hopsworks documentation serves as an essential resource for users and stakeholders to access every aspect of the platform quickly and efficiently.
Whether looking for concepts or APIs, Hopsworks brings comprehensive and accessible documentation with code snippets, examples, and tutorials, enabling you to bring your ML projects to production faster.
A secure and trustworthy platform that allows you to control your data.
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