This keeps the models up-to-date and improves the accuracy of predictions. 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. Additionally, the performance advantage over Redshift ML increases when training is done on a larger HeatWave cluster. On average, on the smallest cluster, HeatWave ML trains machine learning models 25 times faster at one percent of the cost of Redshift ML. 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. No other cloud database vendor provides such advanced ML capabilities directly inside their database service. All models generated by HeatWave ML can provide model and prediction explanations. 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. 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. Finally, most existing ML solutions don't include functionality to provide explanations about why the models that developers build deliver specific predictions. 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. 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. This approach creates multiple silos for applying machine learning to application data and introduces latency as data moves around. First, there is the process of extracting data out of the database and into another system to create and deploy ML models. Until now, adding machine learning capabilities to MySQL applications has been prohibitively difficult and time consuming for many developers. HeatWave ML is included with the MySQL HeatWave database cloud service in all 37 Oracle Cloud Infrastructure (OCI) regions. Eliminating ETL reduces application complexity, lowers cost, and improves security of both the data and the model. 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. HeatWave ML is 25 times faster than Amazon Redshift ML at one percent of the costĪUSTIN, Texas, Ma/PRNewswire/ - 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. HeatWave ML fully automates model training, inference, and explanation Oracle Announces MySQL HeatWave ML-the Easiest, Fastest, and Least Expensive Way for Developers to Add Powerful Machi.
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