HeatWave Release Notes
The MySQL HeatWave AutoML ML_TRAIN routine
leverages Oracle AutoML technology to automate the process of
training a machine learning model. Oracle AutoML replaces the
laborious and time consuming tasks of the data analyst, whose
workflow is as follows:
Selecting a model from a large number of viable candidate models.
For each model, tuning hyperparameters.
Selecting only predictive features to speed up the pipeline and reduce over-fitting.
Ensuring the model performs well on unseen data (also called generalization).
Oracle AutoML automates this workflow, providing you with an optimal
model given a time budget. When you run the MySQL HeatWave AutoML
ML_TRAIN routine, that triggers
the Oracle AutoML pipeline to run the following stages in a single
command:
Data pre-processing
Algorithm selection
Adaptive data reduction
Hyperparameter optimization
Model and prediction explanations
Oracle AutoML also produces high quality models very efficiently, which is achieved through a scalable design and intelligent choices that reduce trials at each stage in the pipeline.
Scalable design: The Oracle AutoML pipeline is able to exploit both MySQL HeatWave internode and intranode parallelism, which improves scalability and reduces runtime.
Intelligent choices reduce trials in each stage: Algorithms and parameters are chosen based on dataset characteristics, which ensures that the model is accurate and efficiently selected. This is achieved using meta-learning throughout the pipeline.
For additional information about Oracle AutoML, refer to Yakovlev, Anatoly, et al. "Oracle AutoML: A Fast and Predictive AutoML Pipeline." Proceedings of the VLDB Endowment 13.12 (2020): 3166-3180.
-
Learn more about the following:
Learn how to Create a Machine Learning Model.
Review Machine Learning Use Cases to create machine learning models with sample datasets.