In addition to updating MySQL HeatWave’s AutoML and Autopilot, Oracle will now offer a small shape for the service, targeting customers with smaller volumes of data.
Oracle is adding new machine learning features to its data analytics cloud service MySQL HeatWave.
MySQL HeatWave combines OLAP (online analytical processing), OLTP (online transaction processing), machine learning, and AI-driven automation in a single MySQL database.The new machine learning capabilities will be added to the service’s AutoML and MySQL Autopilot components, the company said when it announced the update on Thursday.
While AutoML allows developers and data analysts to build, train and deploy machine learning models within MySQL HeatWave without moving to a separate service for machine learning, MySQL Autopilot provides machine learning-based automation to HeatWave and OLTP such as auto provisioning, auto encoding, auto query plan, auto shape prediction and auto data placement, among other features.The new machine learning-based capabilities added to AutoML include multivariate time series forecasting, unsupervised anomaly detection, and recommender systems, Oracle said, adding that all the new features were generally available.
“Multivariate time series forecasting can predict multiple time-ordered variables, where each variable depends both on its past value and the past values of other dependent variables. For example, it is used to build forecasting models to predict electricity demand in the winter considering the various sources of energy used to generate electricity,” said Nipun Agarwal, senior vice president of research at Oracle.In contrast to the regular practice of having a statistician trained in time-series analysis or forecasting to select the right algorithm for the desired output, AutoML’s multivariate time series forecasting automatically preprocesses the data to select the best algorithm for the ML model and automatically tunes the model, the company said.