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.
Oracle 在整合式向量資料庫導入了生成式 AI，並大幅提升開發人員的生產力
Oracle Database 23c 中新的 AI 向量相似度搜尋，可以結合使用語意和業務資料中的搜尋，快速且安全地提供精確的答案。
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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
Introducing natural language to SQL generation on Autonomous Database
Mark Hornick, Senior Director, Data Science and Machine Learning, Oracle
We’re pleased to announce Autonomous Database Select AI, a new capability that lets you query your data using natural language. Select AI combines generative AI with SQL so you can describe what you want—declarative intent—without specialized SQL knowledge and let the database generate the SQL query for you.
Read the complete postResources
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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