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Oracle MySQL HeatWave now supports machine learning

By Ryan Morris-Reade
Thu 31 Mar 2022

Oracle has announced that Oracle MySQL HeatWave now supports in-database machine learning (ML), in addition to the previously available transaction processing and analytics.

MySQL HeatWave ML fully automates the machine learning 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. 

The company says eliminating ETL reduces application complexity, lowers cost, and improves the 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.

"Until now, adding machine learning capabilities to MySQL applications has been prohibitively difficult and time-consuming for many developers," the company says.

"First, there is the process of extracting data out of the database and into another system to create and deploy machine learning 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 machine learning model training process; otherwise, the model is suboptimal, which degrades the accuracy of predictions. Finally, most machine learning solutions don't include functionality to explain why the model's 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. It creates a model with the best algorithm, optimal features, and the optimal hyperparameters for a given data set and a specified task. All models generated by HeatWave ML can provide model and prediction explanations.

Oracle published machine learning benchmarks performed across many 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 1% of the cost of Redshift machine learning. 

"Just as we integrated analytics and transaction processing within a single database, we are now bringing machine learning inside MySQL HeatWave," says Oracle chief corporate architect, Edward Screven.

"MySQL HeatWave is one of the fastest-growing cloud services at Oracle. Many 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 several other innovations which enrich HeatWaves capabilities, improve availability, and lower the cost," he says. 

"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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