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.
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.
How to run a Python program (from inside Hopsworks) that acts as an opensearch-py client for the OpenSearch cluster in Hopsworks.
Create Snowflake, BigQuery and Hopsworks feature groups and then combine them in a unified view exposing all features together regardless of their source.
Real time feature computation using Apache Flink and Hopsworks Feature Store.
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.
This example of Python Hopsworks API program, shows how to create a job for an existing python or spark program, execute it and access application logs.
How to register sklearn.pipeline with transformation functions and classifier in Hopsworks Model Registry and use it in training and inference pipelines.