Use Cases
OpenMLDB is Driving the AI Transformation for Enterprises
Practice of Artificial Intelligence for IT Operations Based on OpenMLDB in a Financial Institution
Building the next-generation AIOps powered by OpenMLDB with resource consumption significantly reduced, scaling the business at low cost
OpenMLDB Helps Build an AI-Powered Anti-Fraud System in Banking Affairs
Helping the commercial bank leverage AI to build an anti-fraud system, driving the AI-based anti-fraud to achieve effectiveness and efficiency
Akulaku: Real-Time Feature Extraction for AI-Powered Risk Control
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.
OpenMLDB Pulsar Connector: Efficiently Connect Real-Time Data to Feature Engineering
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.
OpenMLDB Hive Connector, connecting the data warehouse and feature engineering
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.