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.
Predict the electricity prices in several Swedish cities based on weather conditions, previous prices, and Swedish holidays.
Real-time feature computation using Bytewax.
Real time feature computation using Apache Beam, Google Cloud Dataflow and Hopsworks Feature Store.
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.
How to upload data to your cluster and download data from the cluster to your local environment.
Introduction to Great Expectations concepts and classes which are relevant for integration with the Hopsworks MLOps platform.