TLDR; Feature Stores have become the key piece of data infrastructure for machine learning platforms. They manage the whole lifecycle of features: from training different models to providing low-latency access to features by online-applications for model inference. This article introduces the Hopsworks Feature Store for Databricks, and how it can accelerate and govern your model development and operations on Databricks.

What is a Feature Store?

The Feature Store for machine learning is a feature computation and storage service that enables features to be registered, discovered, and used both as part of ML pipelines as well as by online applications for model inferencing. Feature Stores are…


TLDR; If AI is to become embedded in the DNA of Enterprise computing systems, Enterprises must first re-align their machine learning (ML) development processes to include data engineers, data scientists and ML engineers in a single automated development, integration, testing, and deployment pipeline. This blog introduces platforms and methods for continuous integration (CI), continuous delivery (CD), and continuous training (CT) with machine learning platforms, with details on how to do CI/CD machine learning operations (MLOps) with a Feature Store. …


Feature stores are key components in enterprises’ machine learning/artificial intelligence architectures. In previous blog posts (Introduction to feature store, MLOps with a feature store, and Hopsworks Feature Store for Databricks) we focused on describing the key concepts and building blocks of the Hopsworks Feature Store. In this blog post we are going to focus on how to integrate AWS SageMaker with Hopsworks. Hopsworks is available on AWS as either a SaaS platform (www.hopsworks.ai) or as a custom Enterprise platform.

While Hopsworks provides all the tools to design and operate pipelines that go from raw data to serving models in production…

Fabio Buso

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