With support to Apache Hudi, the Hopsworks Feature Store offers lakehouse capabilities to improve automated feature pipelines and training pipelines (MLOps).
Read about how the Hopsworks Feature Store abstracts away the complexity of a dual database system, unifying feature access for online and batch applications.
Operational machine learning requires the offline and online testing of both features and models. In this article, we show you how to design, build, and run test for features.
This blog introduces platforms and methods for continuous integration (CI), delivery (CD), and training (CT) with ML platforms, with details on how to do CI/CD MLOps with a Feature Store.
In this blog, we describe how we leverage the authentication and authorization support in Open Distro for Elasticsearch to make elasticsearch a project-based multi-tenant service in Hopsworks.
We introduce how to use the What-If Tool as a Jupyter plugin on Hopsworks to build better machine learning models by making it easier to ask counterfactual questions about your model’s behaviour.
In this blog, we discuss the state-of-the-art in data management and machine learning pipelines (within the wider field of MLOps) and present the first open-source feature store, Hopsworks.
The feature store is a data warehouse of features for machine learning (ML). Architecturally, it differs from the traditional data warehouse in that it is a dual-database.
We have many conversations with companies and organizations who are deciding between building their own feature store and buying one. We thought we would share our experience of building one.
Deep learning is now the state-of-the-art technique for identifying financial transactions suspected of money laundering. It delivers a lower number of false positives and with higher accuracy.
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