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.
Build a machine learning model with Weights & Biases.
Predict customers that are at risk of churning.
Predict the electricity prices in several Swedish cities based on weather conditions, previous prices, and Swedish holidays.
Introduction to Great Expectations concepts and classes which are relevant for integration with the Hopsworks MLOps platform.
This Python Hopsworks example shows how to interact with HopsFS from python, using the hdfs module.
How to register sklearn.pipeline with transformation functions and classifier in Hopsworks Model Registry and use it in training and inference pipelines.