Oracle HeatWave AutoML provides integrated, automated, and secure machine learning (ML)—helping you build, train, and explain ML models without ML expertise, data movement, or additional cost. It’s available on Oracle Cloud Infrastructure (OCI), Amazon Web Services (AWS), and Microsoft Azure.
Watch the replay of Chief Corporate Architect Edward Screven’s Oracle CloudWorld keynote: “Build Generative AI Applications—Integrated and Automated with HeatWave GenAI.”
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Nucleus Research analysts interviewed multiple organizations using HeatWave and reported significant operational improvements, including a hundredfold boost for hybrid OLTP/OLAP queries.
Eliminate complex and time-consuming data movements to a separate ML service with integrated ML. Easily apply ML training, inference, and explanation to data stored either in MySQL Database or object storage.
Automate the ML lifecycle, including algorithm selection, intelligent data sampling for model training, feature selection, and hyperparameter optimization. No ML expertise is required.
Keep your data in one data management system with a single security configuration and centralized access controls. All communications are authenticated and encrypted.
Train ML models up to 25X faster than Amazon Redshift ML at 1% of the cost, letting you retrain models more often and get more accurate results.
HeatWave AutoML supports anomaly detection, forecasting, classification, regression, and recommender system tasks, including on text columns.
By considering both implicit feedback (such as past purchases and browsing behavior) and explicit feedback (such as ratings and likes), the HeatWave AutoML recommender system can help, for example, generate personalized next purchase suggestions.
All the models trained by HeatWave AutoML are explainable. HeatWave AutoML delivers predictions with an explanation of the results, supporting you with trust, fairness, and regulatory compliance.
Data drift detection helps analysts determine when to retrain models by detecting the differences between the data used for training and new incoming data.
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. They can also easily explore what-if scenarios to evaluate business assumptions.
HeatWave AutoML is integrated with popular notebooks, such as Jupyter and Apache Zeppelin.
Business analysts and developers without ML expertise can use HeatWave AutoML to help predict customer churn. The ML lifecycle is automated and data doesn’t leave the database, helping to reduce security risks. Once built, the model can predict the probability of customer churn.
Business analysts and developers without ML expertise can use HeatWave AutoML to help detect fraudulent transactions. The ML lifecycle is automated and data doesn’t leave the database, helping to reduce security risks. Once built, the model can predict the probability of fraud associated with transactions.
Developers can build applications leveraging the combined power of built-in ML and generative AI in HeatWave to deliver personalized recommendations. In this example, the application uses the HeatWave AutoML recommender system to help suggest restaurants based on the user’s preferences or what the user previously ordered. With HeatWave Vector Store, the application can help additionally search through restaurants’ menus in PDF format to suggest specific dishes, providing greater value to customers.
“HeatWave does machine learning the right way. By bringing ML to the data with HeatWave AutoML in a cost-efficient, automated way, HeatWave accelerates ML adoption.”
“The in-database HeatWave AutoML makes Redshift ML look like yesterday’s tech in terms of engineering, performance, and cost.”
“I believe the automation built into HeatWave AutoML will make it tangibly easier for customers to use, extending ML beyond the realm of data scientists.”
“With HeatWave AutoML, machine learning is democratized, it’s fast, uses up-to-date data, and costs less than other cloud database services.”
Access the documentation to easily get started with HeatWave AutoML.
Experience HeatWave AutoML at your own pace with step-by-step instructions.
You’ll learn how to build a predictive ML model using HeatWave AutoML.
You’ll build MovieHub, a fictitious movie streaming application that delivers personalized movie recommendations using HeatWave AutoML.
Request a free expert-led workshop to evaluate HeatWave AutoML or get started with it.
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