The OpenMLDB community has recently released a new open-source feature platform product - FeatInsight (https://github.com/4paradigm/FeatInsight).

In the report "The Logical Feature Store: Data Management for Machine Learning" published by the International Authoritative Consulting and Research firm, Gartner, OpenMLDB is honored to be selected as the sole representative feature store vendor from China.

OpenMLDB provides a real-time feature computing platform that ensures consistency between online and offline environments.

The latest issue of "Communications of the ACM" by the Association for Computing Machinery (ACM) published an article on the open-source machine learning database project, OpenMLDB, which received unanimous recognition from the magazine's editorial board.

v0.8.0 新版本发布

The introduction of OpenMLDB Hive Connector allows you to continues to build an offline data ecosystem. This article is expected to build a more comprehensive ecosystem, which can lower the threshold of users while attracting more users and the development has solved the issue of being unable to connect easily and using Hive data sources in OpenMLDB.

One of the key points for the engineering implementation of artificial intelligence is to solve the problems of real-time batch estimation and real-time model updating of real business scenarios. Better and faster transformation of online real-time data into AI usable features will accelerate the efficiency and effect of AI application landing. To this end, OpenMLDB and Apache Pulsar jointly launched the OpenMLDB Pulsar Connector to achieve stable streaming integration and provide a clear path to efficiently get through real-time data to feature engineering.

As may people tends to pay less attention to feature engineering compared to machine learning model. The famous stating that data and features determine the upper limit of machine learning, while models and algorithms only approach its upper limit. In this article, you will know the in importance of feature engineering to extract useful feature information from the original data.

Recently, we have officially released the version 0.4.0 of OpenMLDB. This version also has many performance and function optimizations. Therefore, this article will introduce some new features of the latest version of OpenMLDB 0.4.0 and how to quickly build a full process online AI application based on thi

This article is based on Lu Mian's speech in "OpenMLDB Meetup No.1", sharing mainly includes three contents: 1.Background Introduction: The challenge of data and characteristics of AI engineering landing. 2.OpenMLDB Provides Enterprises with Full Stack FeatureOps Solutions: Why do you want to make 3.FeatureOps, why do you want to have OpenMLDB, and what are some of the main features of OpenMLDB. OpenMLDB's Current Open-source Status, Development Status and Features of Version 0.4.0.

In financial technology scenarios, OpenMLDB not only doubles the team's human efficiency and saves millions of costs, but also is the only solution with linear scale compared with Spark and Flink.

Helping the commercial bank leverage AI to build an anti-fraud system, driving the AI-based anti-fraud to achieve effectiveness and efficiency

Building the next-generation AIOps powered by OpenMLDB with resource consumption significantly reduced, scaling the business at low cost