“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.
Generative AI and vector store (private preview)
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 AutoML
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:
Support for HeatWave Lakehouse: Customers can now leverage HeatWave AutoML for training, inference, and explanations on data in object storage in addition to data in the MySQL database—and use a much wider set of data for machine learning.
Text column support: Enables customers to perform machine learning tasks—anomaly detection, forecasting, classification, regression, and recommender system—on text columns, further broadening the corpus of data on which customers can leverage HeatWave AutoML.
Enhanced recommender system: With support for Bayesian Personalized Ranking (BPR), HeatWave AutoML can now consider both implicit feedback (past purchases, browsing behavior) and explicit feedback (ratings, likes) to generate personalized recommendations. As an example, analysts can predict items a user will like, users who will like a specific item, and ratings items will receive.
Training Progress monitor: Customers can now monitor the progress of the model training with HeatWave AutoML, allowing them to better manage resources