.svg)
Hopsworks brings Advanced Machine Learning and Data Science Platform to Google Cloud Platform
In a significant stride towards fostering AI innovation in the Nordics, Hopsworks has partnered with AI Sweden to provide a "testbed" environment to all of AI Sweden Partners. The collaboration aims to foster the adoption of cutting-edge AI technologies among Nordic businesses and institutional actors.
I ett betydande steg mot att främja AI-innovation i Norden har Hopsworks ingått ett partnerskap med AI Sweden för att erbjuda en testbädd till alla AI Swedens partners. Samarbetet syftar till att främja användningen av den senaste AI-tekniken bland nordiska företag och institutionella aktörer.
Hopsworks has partnered with OpenSearch, an open-source, vector database for building flexible, scalable, and future-proof AI applications. With this collaboration, OpenSearch is the vector database that powers Hopsworks feature store’s search capabilities for ML assets.
With a tripling of growth in 2022, Hopsworks raised $6.5M in investment and brings in Lars Nordwall, former President and COO at the double unicorn Neo4j, as Executive Chair.
Hopsworks recently entered a partnership with MLOps platform Katonic. Katonic’s platform is used to develop, deploy, monitor, and manage advanced analytics and ML and AI solutions in a self-service, collaborative, governed, and secure manner.
Hopsworks has successfully completed the AICPA Service Organization Control (SOC) 2 Type II audit.
Hopsworks has received an ISO 27001 certification, the globally recognized standard for establishing, implementing, maintaining, and continually improving an information security management system.
The company that launched Hopsworks, the world’s first open-source Feature Store for AI, raises a €5M Series A investment led by the Nordic VC Industrifonden with the participation of Inventure. Hopsworks has already attracted industry leading organizations including PaddyPower-Betfair, Getinge, and Swedbank.
Logical Clocks announces three new research projects part of the European Union (EU) Horizon 2020 research and innovation programme that will benefit from Hopsworks artificial intelligence (AI) capabilities to scale deep learning and enhance research focused on understanding environmental changes and improving healthcare in Europe. Hopsworks is the world’s first and most advanced managed Feature Store with an end-to-end AI platform for the development and operation of AI applications at scale.
Mikael Ronström joins Logical Clocks as Head of Data. Mikael Ronström is the inventor and lead developer of NDB Cluster, an open-source distributed database underlying the MySQL Cluster platform.
Logical Clocks introduces a new machine learning technique to train models for fraud detection using deep learning and Generative Adversarial Networks (GANs). The technique, available on Hopsworks, the world’s first data platform with a Feature Store, helped Swedbank, the oldest and largest bank in Sweden, reduce costs associated with fighting fraud.
Logical Clocks announced it is developing the first enterprise Feature Store for Edge Computing for the AI-NET ANIARA project, part of the CELTIC-NEXT programme, to bring artificial intelligence to 5G networks in Europe. To meet infrastructural requirements on performance, security, reliability and scalability, the project will take advantage of Logical Clocks’ Feature Store.
Logical Clocks, the enterprise behind Hopsworks - the first data platform for designing and operating machine learning and artificial intelligence (AI) applications at scale with a Feature Store - announces the launch of Hopsworks.ai, the world’s first managed cloud platform for AI with a feature store.
On September 5th, 2019, Logical Clocks won the European DatSci award for “Data Science Technology Innovation of the Year”. Hopsworks is a data-intensive platform for data science and AI, that includes the first Enterprise Feature Store for Machine Learning.
Announcing the release of the first Enterprise Feature Store for Machine Learning. The Feature Store solves the problem of ad-hoc and siloed machine learning pipelines, where features, the training data for such pipelines, tend to become disorganized, disjointed, and duplicated, leading to correctness problems and redundant work.
