Stockholm, Sweden, Nov. 17, 2020 (GLOBE NEWSWIRE) -- Logical Clocks, the data company behind the world’s first Enterprise Feature Store for Machine Learning, today announced full support for Microsoft Azure on its cloud managed data platform, Hopsworks. The announcement complements the existing support for Amazon Web Service (AWS), thus enterprises can now manage features for training and serving models at scale on Hopsworks while maintaining control of their data inside their organisation’s accounts on the most popular cloud platforms.
Hopsworks is the only currently available cloud-native Enterprise Feature Store and it also includes a Data Science platform for developing and operating machine learning models in production.
“As Hopsworks is a modular platform with integrations to third party platforms, organisations can combine our Hopsworks Feature Store with their existing data stores and data science platforms in their cloud of choice, AWS or Azure,” explains Steffen Grohsschmiedt, Head of Cloud at Logical Clocks.
Hopsworks offers a free version which includes all functionalities needed for feature engineering, feature management, and ML model training. Hopsworks offers unlimited storage based on Azure Blob Storage as well as integrations with Databricks, SageMaker, and Kubeflow.
"Our mission is to hide the complexity of developing and operating machine learning at scale, for every company. We provide data scientists with the tools and platform to create the best possible pipeline for any use case. We are committed to making our platform best of breed and modular enough to work in any ecosystem,” concludes Steffen.
For more information and to register for free: www.hopsworks.ai
About Logical Clocks
Logical Clocks is the data company that builds Hopsworks, the world’s first Enterprise Feature Store for Machine Learning. With offices in Stockholm, London and Palo Alto, Logical Clocks has set out to make machine learning easy for every company, providing tools for data scientists and engineers to manage features and develop/deploy models in production. For more information, access www.logicalclocks.com or follow @LogicalClocks on Twitter.
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