Machine Learning in Oracle AI Database
Machine Learning in Oracle AI Database facilitates scalable data exploration, preparation, and ML modeling using SQL, R, Python, REST, AutoML, and no-code interfaces, offering over 30 high-performance in-database algorithms for immediate application use, and an AI agent for natural language data science workflows
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OML4Py 2.1 Client Now Available for Windows
Oracle Machine Learning for Python (OML4Py) 2.1 client is now available for Windows users, allowing data scientists and developers to build, train, and deploy machine learning models directly from their Windows laptops and workstations, removing the need for a local Linux client or virtual machine
GitHub Integration Now Available for Enhanced OML Notebook Versioning, Syncing, and Sharing
Oracle Machine Learning (OML) Notebooks now directly connects to GitHub repositories, allowing seamless import of OML, Jupyter, and Zeppelin notebooks and synchronization of changes
Enhanced Embedding Model Deployment Options with OML4Py and OML4SQL
Oracle Machine Learning for Python (OML4Py) version 2.1.1 and OML4SQL now support large ONNX embedding models via external data and in-memory sharing in Oracle AI Database 23.26.1, enhancing scalability by increasing model size limits and optimizing memory usage in concurrent environments
Why choose Machine Learning in Oracle AI Database?
Enhance productivity with Oracle AI Database's built-in automation, in-database execution performance, and scalability, while identifying possible data biases and understanding prediction factors
Data Science Agent
Brings a modern, chat-based experience to the data science lifecycle, running in-database with your choice of AI provider and LLM
In-database operations
Accelerate model building and data scoring at scale directly within Oracle Exadata using its scale-out architecture and Smart Scan technology for faster results
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 directly in Oracle AI Database to streamline exploration, preparation, model building, and deployment, reducing development time, complexity, and enhancing data security
No-code model building
Enhance data scientist productivity and empower nonexperts to utilize robust in-database algorithms for classification and regression via a no-code AutoML interface
Data and model monitoring
Gain insights into the evolution of your data and machine learning models to take prompt corrective actions and prevent significant enterprise issues using REST endpoints and no-code interfaces
Rapid enterprise deployment
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.
Built-in security
Benefit from Oracle AI Database's integrated security and encryption, role-based data access, and support for in-database and third-party models, as well as R and Python objects and scripts
Enhanced Embedding Model Deployment Options with OML4Py and OML4SQL
Sherry LaMonica, Consulting MTS, AI and ML Product Management, OracleOracle Machine Learning for Python (OML4Py) version 2.1.1 and OML4SQL now support large ONNX embedding models with external data and in-memory sharing in Oracle AI Database 23.26.1, enhancing scalability by increasing model size limits and optimizing memory use for concurrent users
Featured blogs
- February 2026Introducing Datastore Export and Import in OML4Py 2.1.1
- March 2026New Version of Embedded Execution for Python and R REST and SQL APIs now available
- October 2024Enhance your AI/ML applications with flexible Bring Your Own Model options
- September 2024Announcing Oracle Machine Learning Services Data Bias Detector
Resources
Getting started with machine learning in the cloud
Learn what’s new in the latest release
Free tutorials and GitHub repository
Related information
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Machine Learning in Oracle AI Database reference architectures
- Reference Architecture
With Oracle Autonomous AI Database, you have all the necessary built-in tools to load and prepare data, and to train, deploy, and manage machine learning models. These services are included with Autonomous AI Database, but you also have the flexibility to mix and match other tools to best fit your organization’s needs.
- Reference Architecture
Get the framework to enrich enterprise application data with raw data from other sources and to use machine learning models to bring intelligence and predictive insights into business processes.
- Reference Architecture
Discover the platform topology, component overview, and recommended best practices for implementing a successful data lake house on OCI to capture a wealth of data and aggregate and manage data for real time stock visibility.
- Reference Architecture
Oracle Architecture Center provides vetted design patterns, architecture examples, solution playbooks, and deployment code to help you implement your cloud, multicloud, distributed cloud, and on-premises solutions. Use this information as a guide to get the most out of your investment.
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