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

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

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.

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.

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.