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.
Announcing Oracle Database 23ai: Bring AI to your data
Larry Ellison and Juan Loaiza discuss the GenAI strategy behind Oracle Database 23ai.
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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?
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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 deliver fast results.
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No data movement
Process data where it resides in Oracle Database. This simplifies model building and deployment, shortens application development time, reduces complexity, and improves data security.
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No-code model building
Improve data scientist productivity and let nonexpert users access to powerful in-database algorithms for classification and regression through a no-code AutoML user interface.
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Built-in security
Benefit from Oracle Database’s built-in security, encryption, and role-based access to user data, in-database and third-party models, and R and Python objects and scripts.
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Rapid enterprise deployments
Achieve immediate machine learning model availability with easy deployment options using SQL and REST interfaces.
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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.
Machine Learning in Oracle Database customer successes
Simplify Your Model Monitoring and MLOps with OML Model Monitoring UI
Mark Hornick, Senior Director, Data Science and Machine Learning, OracleOracle Machine Learning Model Monitoring UI is an easy-to-use, no-code user interface on Oracle Autonomous Database. It helps experts and non-experts get insight into how machine learning model performance changes over time and the possible causes of those changes. Find out why model monitoring is vital for your desired business outcomes and how to get started with a few clicks.
Read the complete postFeatured blogs
- January 19, 2024 Autonomous Database Select AI: Accelerate Innovation with Enterprise Data, OCI Generative AI, and Enhanced Security
- January 3, 2024 OML AutoML UI—Click Your Way to an ML Model
- November 10, 2023 Announcing Select AI with Azure OpenAI Service on Oracle Autonomous Database
- August 7, 2023 Announcing OML Data Monitoring User Interface on Autonomous Database
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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