This video shows how you can leverage the Hopsworks online feature store to compute and ingest features and make them available to operational models making real-time predictions, with low latency and preventing skew between the training and serving features.
A feature store is a data platform that manages and governs your features for machine learning - both for training and serving. You have access to and can reuse previously engineered features available within the entire organisation, avoiding the need to write a feature engineering pipeline for every model put in production.
The Hopsworks Feature Store is available today on Azure as both a managed platform (www.hopsworks.ai) and a custom Enterprise installation. It manages your features for training and serving models in a cluster under your control inside your organisation’s existing cloud account.
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The Hopsworks.ai is the world's first managed feature store for the cloud that provides an open-source and general purpose feature store to design and operate AI applications at scale with Apache Spark.
The Hopsworks Feature Store is a platform for managing and serving features for machine learning that also integrates Kubeflow and On-Premises Clusters (Hadoop).
The Hopsworks Feature Store is a platform for managing and serving features for machine learning. It integrates seamlessly with popular platforms for Data Science and cloud-based data lakes based on S3.
All hyperscale AI companies (LinkedIn, Facebook, AiBnB, Microsoft, Google, etc) build their machine learning platforms around a Feature Store.