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Machine Learning in Oracle Database

Machine Learning in Oracle Database supports data exploration, preparation, and machine learning (ML) modeling at scale using SQL, R, Python, REST, automated machine learning (AutoML), and no-code interfaces. It includes more than 30 high performance in-database algorithms producing models for immediate use in applications. By keeping data in the database, organizations can simplify their overall architecture and maintain data synchronization and security. It enables data scientists and other data professionals to build models quickly by simplifying and automating key elements of the machine learning lifecycle.

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Why choose Machine Learning in Oracle Database?

Oracle Database supports data management, model development and deployment options, data and model monitoring, and team collaboration. Enhance productivity through built-in automation, in-database execution performance, and scalability. Identify possible bias in data and understand factors contributing to predictions.

In-database operations

Build models and score data faster and at scale without extracting data to separate analytics engines. Oracle Exadata’s scale-out architecture and Smart Scan technology help deliver results faster.

Multiple language APIs

Choose from SQL, Python, and R interfaces for in-database data exploration and preparation, machine learning modeling, and solution deployment. In addition, deploy Python and R solutions using SQL and REST.

No data movement

Process data where it resides in Oracle Database to simplify data exploration and preparation as well as model building and deployment. Shorten application development time, reduce complexity, and address data security.

No-code model building

Improve data scientist productivity and help nonexperts use powerful in-database algorithms for classification and regression through a no-code AutoML user interface.

Data and model monitoring

Gain insights into how your data and machine learning models evolve over time and take corrective action sooner to avoid issues that can have a significant negative impact on the enterprise. Use REST endpoints and no-code user interfaces.

Rapid enterprise deployments

Achieve immediate machine learning model availability with easy deployment options using SQL and REST interfaces.

Bring your own model

Import text transformer, classification, regression, and clustering models in Open Neural Network Exchange (ONNX) format to use from SQL with the in-database ONNX Runtime. Deploy ONNX format models to Oracle Machine Learning Services for real-time inferencing use cases.

High performance compute

Avoid performance issues during data preparation, model building, and data scoring using the built-in parallelism and scalability of Oracle Database, with unique optimizations for Oracle Exadata.

Built-in security

Benefit from Oracle Database’s built-in security and encryption, role-based access to user data, in-database and third-party models, and R and Python objects and scripts.

Machine Learning in Oracle Database customer successes

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September 10, 2024

Announcing GPU Support for Oracle Machine Learning Notebooks on Autonomous Database

Mark Hornick, Senior Director, Data Science and Machine Learning

Oracle Autonomous Database Serverless now provides integrated access to GPUs through Oracle Machine Learning Notebooks. Develop Python code using the Oracle Machine Learning Notebooks Python interpreter for use cases requiring the performance and scalability of GPUs, such as running vector embedding (transformer) models and building deep learning models for satellite image processing.

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Machine Learning in Oracle Database reference architectures

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  • Reference Architecture

    Machine learning platform on Autonomous Data Warehouse

    With Oracle Autonomous Data Warehouse, you have all the necessary built-in tools to load and prepare data and to train, deploy, and manage machine learning models. You also have the flexibility to mix and match other tools to best fit your organization’s needs.

  • Reference Architecture

    Modern app development—machine learning and artificial intelligence

    Learn the design principles associated with creating a machine learning platform and an optimal implementation path. Use this pattern to create machine learning platforms that meet the needs of your data scientist users.

  • Reference Architecture

    Data platform—data warehouse with complex integration

    Get the framework to enrich enterprise application data with raw data from other sources, and then use machine learning models to bring intelligence and predictive insights into business processes.

  • Reference Architecture

    Design a data lakehouse for retail inventory analytics

    Discover the platform topology, component overview, and recommended best practices for implementing a successful data lakehouse on OCI to capture a wealth of data and aggregate and manage data for real-time stock visibility.

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