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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Announcing Oracle Machine Learning monitoring on Autonomous Database
Prevent data drift and monitor the performance of your machine learning models. New monitoring capabilities within Machine Learning in Oracle Database services alert you to issues in both data and native in-database model quality.
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Use
custom third-party Python and R packages on Autonomous Database
Leverage broader Python and R package ecosystems on Oracle Autonomous Database in Oracle Machine Learning Notebooks. Run user-defined functions with third-party package functionality in engines spawned and managed by the Oracle Database environment.
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Run R
at scale on database data with Oracle Machine Learning for R
Explore, transform, and analyze data faster and at scale while using familiar R syntax and semantics and taking advantage of Oracle Database as a high performance computing environment.
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Embedding AI/ML in your application using Oracle Machine Learning
Deploying and scaling machine learning models and broader Python and R-based solutions in production is often challenging. Learn how to simplify embedding AI and ML in applications using Machine Learning in Oracle Database.
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
Announcing GPU Support for Oracle Machine Learning Notebooks on Autonomous Database
Mark Hornick, Senior Director, Data Science and Machine LearningOracle 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.
Read the complete postFeatured blogs
- September 10, 2024 Announcing Select AI for Synthetic Data Generation
- September 10, 2024 Bring the Power of Graphs to Generative AI
- September 10, 2024 Announcing Oracle Machine Learning Services Data Bias Detector
- September 10, 2024 Now Available: Prebuilt Embedding Generation Model for Oracle Database 23ai
Resources
Getting started with machine learning in the cloud
Learn what’s new in the latest release
GitHub repository
Related info
Machine Learning in Oracle Database reference architectures
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Reference Architecture
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.
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Reference Architecture
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.
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Reference Architecture
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.
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Reference Architecture
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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