HeatWave ML fully automates model training, inference, and explanation

Oracle today announced that Oracle MySQL HeatWave now supports in-database machine learning (ML) in addition to the previously available transaction processing and analytics—the only MySQL cloud database service to do so. MySQL HeatWave ML fully automates the ML lifecycle and stores all trained models inside the MySQL database, eliminating the need to move data or the model to a machine learning tool or service. Eliminating ETL reduces application complexity, lowers cost, and improves security of both the data and the model. HeatWave ML is included with the MySQL HeatWave database cloud service in all 37 Oracle Cloud Infrastructure (OCI) regions.

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Until now, adding machine learning capabilities to MySQL applications has been prohibitively difficult and time consuming for many developers. First, there is the process of extracting data out of the database and into another system to create and deploy ML models. This approach creates multiple silos for applying machine learning to application data and introduces latency as data moves around. It also leads to the proliferation of data out of the database, making it more vulnerable to security threats, and adds complexity for developers to program in multiple environments. Second, existing services expect developers to be experts in guiding the ML model training process; otherwise, the model is sub-optimal, which degrades the accuracy of predictions. Finally, most existing ML solutions don’t include functionality to provide explanations about why the models that developers build deliver specific predictions.

MySQL HeatWave ML solves these problems by natively integrating machine learning capabilities inside the MySQL database, eliminating the need to ETL the data to another service. HeatWave ML fully automates the training process and creates a model with the best algorithm, optimal features, and the optimal hyper-parameters for a given data set and a specified task. All models generated by HeatWave ML can provide model and prediction explanations.

No other cloud database vendor provides such advanced ML capabilities directly inside their database service. Oracle published ML benchmarks performed across a large number of publicly available machine learning classification and regression datasets such as Numerai, Namao, and Bank Marketing, among others. On average, on the smallest cluster, HeatWave ML trains machine learning models 25 times faster at one percent of the cost of Redshift ML. Additionally, the performance advantage over Redshift ML increases when training is done on a larger HeatWave cluster. Training is a time-consuming process and since it can be done very efficiently and rapidly with MySQL HeatWave, customers can now retrain their models more often and keep up with changes to data. This keeps the models up-to-date and improves the accuracy of predictions.

“Just as we integrated analytics and transaction processing within a single database, we are now bringing machine learning inside MySQL HeatWave,” said Edward Screven, chief corporate architect, Oracle. “MySQL HeatWave is one of the fastest growing cloud services at Oracle. An increasing number of customers have migrated from Amazon and other cloud database services to MySQL HeatWave and have gained significant performance improvements and lower costs. Today, we are also announcing a number of other innovations which enrich HeatWave’s capabilities, improve availability, and lower the cost. Our new and fully transparent benchmark results again demonstrate that Snowflake, AWS, Microsoft, and Google are slower and more expensive than MSQL HeatWave by a large margin.”

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