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 implement feature monitoring in your production pipeline.
Introduction to Great Expectations concepts and classes which are relevant for integration with the Hopsworks MLOps platform.
This example Hopsworks program shows the Python API for managing secrets in Hopsworks.
How to register Sklearn Transformation Functions and PyTorch model in the Hopsworks Model Registry, how to retrieve them and then use in training and inference pipelines.
Build a recommender system for fashion items based on data from previous transactions, as well as from customer and product meta data.
Predict Bitcoin price using time series features and tweets sentiment analysis.