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. The Feature Store solves the problem of siloed machine learning pipelines, where features are independently re-implemented in organizations leading to correctness problems and duplicated work.
The 4th Annual DatSci were hosted in Dublin, Ireland on 5th September 2019. The Awards demonstrates and celebrates the best of European Data Science and Artificial Intelligence in driving real business, educational and social outcomes.
“The award is a wonderful recognition of the quality of the Hopsworks platform, its Feature Store, and how it enables Data Scientists to not just design but also operate Machine Learning applications at scale. It also demonstrates the Enterprise readiness of the platform and how, in Europe, it meets the stringent standards for Machine Learning Governance – the exercise of authority and control (access, monitoring, auditing, and provenance) over the management of machine learning assets. Also, Hopsworks makes Data Scientists more productive: it gives you repeatable notebooks that you can just drop into a production workflow, a framework to manage experiments and models and tools to help you make the transition from design to operation.” Dr. Jim Dowling (CEO) said.
About Logical Clocks AB
Logical Clocks was founded by the team that created and continues to drive Hopsworks, a Data-Intensive AI platform, and its Feature Store, a warehouse for machine learning features. Logical Clocks’ vision is to simplify the process of refining data into intelligence at scale. Logical Clocks has offices in Stockholm, London, and Palo Alto. For more information, visit https://www.logicalclocks.com.
Hopsworks 1.x series brings many new features and improvements, ranging from services such as the Feature Store and Experiments, to enhanced support for distributed stream processing and analytics with Apache Flink and Apache Beam, to building Deep Learning pipelines with TensorFlow Extended (TFX), to code versioning support for Jupyter notebooks with Git, to all-new provenance/lineage of data across all steps of a data engineering and data science. We are also excited that Hopsworks 1.x is the back-bone of the all new Managed Hopsworks platform for AWS, Hopsworks.ai (https://www.hopsworks.ai/).
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.
Hopsworks 0.10 brings the latest features, improvements and bug fixes. It is the biggest release done so far, made up of 191 JIRAs including many new features. Also, this version marks the last of the 0.x series, as Hopsworks is gearing up towards its 1.x series starting with 1.0 end of Q3 2019.
Hopsworks 0.9.0 brings the latest features, improvements and bug fixes. It introduces Apache Airflow as-a-service which means users can now create their own workflows from within their familiar environment of a Hopsworks project. You can get started with Airflow in Hopsworks by visiting the user-guide.
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.
Hopsworks 0.8.0 brings the latest features, improvements and bug fixes. It comes a short while after version 0.7.0 and brings the world’s first open-source feature store, a revamped REST API for managing jobs in Hopsworks and improvements in visualization for python notebooks.