“Today’s enhancements to MySQL HeatWave are another significant step on our journey to address pressing customer data, analytics, and AI issues,” said Edward Screven, chief corporate architect, Oracle. “We’ve previously added real-time analytics with the best price-performance in the industry, automated machine learning, lakehouse, and multicloud capabilities to HeatWave. Now vector store and generative AI bring the power of LLMs to customers, providing them with an intuitive way to interact with data in their enterprise and get the accurate answers that they need for their business.”
For customers looking to perform analytics, transaction processing, machine learning, and generative AI across a variety of data types and sources, additional capabilities have been added to MySQL HeatWave—for both MySQL-compatible and non-MySQL workloads.
The vector store ingests documents in a variety of formats such as PDF and stores them as embeddings generated via an encoder model. For a given user query, the vector store identifies the most similar documents by performing a similarity search over the stored embeddings and the embedded query. These documents are used to augment the prompt given to the LLM so that it provides a more contextual answer.
MySQL HeatWave provides in-database machine learning with a fully automated pipeline for training models. Customers don’t need to move data to a separate machine learning service; they can easily and securely apply machine learning training, inference, and explanation to data stored inside MySQL HeatWave. The following new capabilities have been added:
MySQL Autopilot is a built-in capability of MySQL HeatWave that uses machine learning-powered automation to help improve performance and scalability without requiring database tuning expertise. It learns from the execution of queries to improve the execution plan of future queries. The latest enhancements to MySQL Autopilot include:
“The MySQL HeatWave engineering team is clearly doubling down on AI and machine learning innovation,” said Steve McDowell, principal analyst and founding partner, NAND Research. “Not only can customers now train ML models on data both in the database and in object storage with full automation, but with the new generative AI and vector store capabilities they’ll be able to interact with HeatWave in natural language, and they’ll receive accurate answers for their own business purposes only—based on their own enterprise data in addition to publicly available data. The flexibility to use whichever LLMs organizations prefer continues to demonstrate the open and collaborative approach of the MySQL HeatWave engineering team.”
MySQL HeatWave is the only cloud service that provides transaction processing, real-time analytics, machine learning, data lake querying, and machine learning-powered automation within a single MySQL database service. A core part of Oracle’s distributed cloud strategy, MySQL HeatWave is available natively on OCI and Amazon Web Services, as part of the Oracle Database Service for Azure, and in customers’ data centers with OCI Dedicated Region.
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