Oracle Corp. is adding more wood to the MySQL database fire that it started with the introduction of HeatWave in 2020. The company has 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.
“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.”,said Edward Screven, chief corporate architect, Oracle.
HeatWave ML offers the following capabilities:
- Fully Automated Model Training
- Model and Inference Explanations
- Hyper-Parameter Tuning
- Algorithm Selection
- Intelligent Data Sampling
- Feature Selection
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, the 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 (Extract, Transform and Load) 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.
Oracle also announced new features for MySQL HeatWave, including real-time elasticity that allows scaling of HeatWave clusters up or down to any number of nodes with no downtime or need for manually rebalancing the cluster. The company also announced data compression, a feature it says enables processing of twice the data per node at half the cost. There is also a new pause-and-resume function to allow customers to pause HeatWave for cost savings, with the added capability of automatic reloading of the required MySQL Autopilot data and statistics upon resumption.
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