MySQL HeatWave AutoML

Oracle MySQL 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.

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Why use MySQL HeatWave AutoML?

  • Build applications faster with integrated ML

    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.

  • Democratize machine learning

    Automate the ML lifecycle, including algorithm selection, intelligent data sampling for model training, feature selection, and hyperparameter optimization. No ML expertise is required.

  • Support data security

    Keep your data in one data management system with a single security configuration and centralized access controls. All communications are authenticated and encrypted.

  • Get more accurate results at lower cost

    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.

Key features of MySQL HeatWave AutoML

Comprehensive ML capabilities

MySQL HeatWave AutoML supports anomaly detection, forecasting, classification, regression, and recommender system tasks, including on text columns.

Built-in recommender system

By considering both implicit feedback (such as past purchases and browsing behavior) and explicit feedback (such as ratings and likes), the MySQL HeatWave AutoML recommender system can help, for example, generate personalized next purchase suggestions.

Explanable ML models

All the models trained by MySQL HeatWave AutoML are explainable. MySQL HeatWave AutoML delivers predictions with an explanation of the results, supporting you with trust, fairness, and regulatory compliance.

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.

Interactive 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. They can also easily explore what-if scenarios to evaluate business assumptions.

Integrated with popular tools

MySQL HeatWave AutoML is integrated with popular notebooks, such as Jupyter and Apache Zeppelin.

MySQL HeatWave AutoML customer successes

Use cases for MySQL HeatWave AutoML

Business analysts and developers without ML expertise can use MySQL 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.


Predict customer churn diagram, description below:

The user states that his/her use case is “I need the ability to predict customer churn”. He or she can then easily leverage the MySQL HeatWave AutoML automation to build a classification machine learning model, which is appropriate in this case. Once done, the user can use the ML model, for example asking, “How likely is this customer to churn?” and getting the answer “The probability that this customer will churn is 72%.”



Business analysts and developers without ML expertise can use MySQL 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.


Detect fraudulent transactions diagram, description below:

The user states that his/her use case is “I need to detect potentially fraudulent transactions.”. He or she can then easily leverage the MySQL HeatWave AutoML automation to build an anomaly detection machine learning model, which is appropriate in this case. Once done, the user can use the ML model, for example asking, “Which of these transactions are likely to be fraudulent?” and getting the answer “Here are the transactions identified as potentially fraudulent with associated probabilities.”



Developers can build applications leveraging the combined power of built-in ML and generative AI in MySQL HeatWave to deliver personalized recommendations. In this example, the application uses the MySQL HeatWave AutoML recommender system to help suggest restaurants based on the user’s preferences or what the user previously ordered. With MySQL HeatWave Vector Store, the application can help additionally search through restaurants’ menus in PDF format to suggest specific dishes, providing greater value to customers.


RAG enhanced with ML diagram, description below:

A user asks via MySQL HeatWave Chat “What vegan dishes do you suggest for me today?”. First, the MySQL HeatWave AutoML recommender system suggests a list of restaurants based on what the user previously ordered. Then, MySQL HeatWave Vector Store provides an augmented prompt to the LLM based on the restaurants’ menus that it houses. The LLM can then generates a personalized recommendation of dishes in natural language.



See what top industry analysts say about MySQL HeatWave AutoML

  • Constellation Research logo

    “MySQL HeatWave does machine learning the right way. By bringing ML to the data with MySQL HeatWave AutoML in a cost-efficient, automated way, MySQL HeatWave accelerates ML adoption.”

    Holger Mueller
    Vice President and Principal Analyst, Constellation Research
  • The Futurum Group logo

    “The in-database MySQL HeatWave AutoML makes Redshift ML look like yesterday’s tech in terms of engineering, performance, and cost.”

    Ron Westfall
    Senior Analyst and Research Director, The Futurum Group
  • Moor Insights & Strategy logo

    “I believe the automation built into MySQL HeatWave AutoML will make it tangibly easier for customers to use, extending ML beyond the realm of data scientists.”

    Matt Kimball
    Vice President and Principal Analyst, Moor Insights & Strategy
  • Enterprise Strategy Group logo

    “With MySQL HeatWave AutoML, machine learning is democratized, it’s fast, uses up-to-date data, and costs less than other cloud database services.”

    Mike Leone
    Practice Director, Data Analytics and AI, Enterprise Strategy Group

Learn more about other MySQL HeatWave solutions for your different workloads

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Documentation

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You’ll learn how to build a predictive ML model using MySQL HeatWave AutoML.

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Build a movie recommendation application with MySQL HeatWave AutoML

You’ll build MovieHub, a fictitious movie streaming application that delivers personalized movie recommendations using MySQL HeatWave AutoML.

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