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Python
Fastest Performance
Develop & Deploy models faster, with ironclad data governance and seamless integrations. On-premises, fully managed or on any cloud.
We are proud to introduce the AI Lakehouse, the first unified tool specifically designed for building AI systems.
Increased model performance in production. More models, less drifts, better testing.
Single digit millisecond latency in reading features. Ultra-low latency in writing, and updating.
Sovereignty on private clouds & infrastructures. Better governance and metadata management.
Easily integrate with existing teams, tools, frameworks, and systems, across any environment.
Open standards in file formats, processes, and languages across all tools, from orchestration to training.
Straightforward APIs, minimal setup, no lock-in, and no need for DSL or specialized languages.
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.
How to register custom transformation functions in hopsworks feature store use then in training and inference pipelines.
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.
Predict the fare amount for a taxi ride in New York City given the pickup and drop off locations.
Create an External Feature Group using GCS Storage Connector.
Real time feature computation using Apache Beam, Google Cloud Dataflow and Hopsworks Feature Store.
How to implement feature monitoring in your production pipeline.
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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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