HeatWave AutoML Features

Integrated machine learning (ML)

Oracle HeatWave AutoML includes everything users need to build, train, and explain machine learning models within HeatWave, at no additional cost. With integrated ML in HeatWave, you don’t need to move data to a separate ML service. You can easily and securely apply ML training, inference, and explanation to data stored both inside MySQL Database and in object storage with the help of Oracle HeatWave AutoML. As a result, it helps you accelerate ML initiatives, increase security, and reduce costs.

Automated ML lifecycle

HeatWave AutoML automates the ML lifecycle, including algorithm selection, intelligent data sampling for model training, feature selection, and hyperparameter optimization—helping with saving data analysts and data scientists significant time and effort. Aspects of the ML pipeline can be customized, including algorithm selection, feature selection, and hyperparameter optimization. HeatWave AutoML supports anomaly detection, forecasting, classification, regression, and recommender system tasks, including on text columns. Users can provide feedback on the results of unsupervised anomaly detection and use this labeled data to help improve subsequent predictions.

Recommender system for personalized recommendations

Recommendation systems are one of the most sought-after ML applications. By considering both implicit feedback (past purchases, browsing behavior, and so forth) and explicit feedback (ratings, likes, and so forth), the HeatWave AutoML recommender system can generate personalized suggestions. Analysts, for instance, can predict items that a user will like, users who will like a specific item, and ratings that items will receive. They can also, given a user, obtain a list of similar users and, given a specific item, obtain a list of similar items.

Interactive HeatWave AutoML console

The interactive console lets business analysts build, train, run, and explain ML models using a visual interface—there’s no need to know SQL commands or coding. The console also makes it easy for users to explore what-if scenarios to evaluate business assumptions—for example, “How would investing 30% more in paid social media advertising affect both revenue and profit?”

Explainable ML models

All the models trained by HeatWave AutoML are explainable. HeatWave AutoML helps deliver predictions with an explanation of the results, helping organizations with regulatory compliance, fairness, repeatability, causality, and trust.

Topic modeling

Topic modeling helps users discover insights in large textual data sets by helping them understand key themes in documents, for example, to complete sentiment analysis on social media data.

Data drift detection

Data drift detection helps analysts determine when to retrain models by detecting the differences between the data used for training and new incoming data.

Faster, less expensive, and more accurate than Redshift ML

Benchmarks demonstrate that, on average, HeatWave AutoML helps produce more accurate results than Amazon Redshift ML, trains models up to 25X faster at 1% of the cost, and scales as more nodes are added. As a result, you can retrain models more often to improve accuracy.

See the benchmark details

Use current skills

Developers, data analysts, and data scientists can build ML models using familiar SQL commands; they don’t have to learn new tools and languages. Additionally, HeatWave AutoML is integrated with popular notebooks, such as Jupyter and Apache Zeppelin.

Read the HeatWave AutoML technical brief (PDF)