AutoML Automated machine learning (AutoML) helps all data scientists by automating algorithm selection, data and feature selection, and model tuning. This enables faster time to results, more accurate and reliable results, and less compute time.

In-database optimized algorithms Oracle Database includes more than 30 high-performance, fully scalable algorithms covering commonly used machine learning techniques, such as anomaly detection, regression, classification, clustering, and more. Data already in Oracle Database does not need to be moved, reducing the data management workload for data scientists and allowing them to focus on building production models.

Open source libraries and frameworks Use and import open source libraries and frameworks of choice to enable data transformation, visualization, and model building. These include, but are not limited to: pandas, Dask, and NumPy for transformation, Seaborn, Plotly, and Matplotlib for visualization, and TensorFlow, Keras, and PyTorch for model building.

Choice of deployment Quickly deploy models for access by applications and business analysts. Models can be deployed with a REST API in a serverless, scalable cloud architecture as Oracle Functions or directly in the database.

Model explanation Model explanation enables experts and nonexperts alike to understand what caused a model to return a particular result. With model explanation, it’s easy to understand the importance of features, and how to generate more, or less, of an outcome.