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.
New AI vector similarity search in Oracle Database 23c allows the combination of search in semantic and business data to provide highly accurate answers quickly and securely.
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.
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.
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.
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.
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.
Process data where it resides in Oracle Database. This simplifies model building and deployment, shortens application development time, reduces complexity, and improves data security.
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.
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.
Achieve immediate machine learning model availability with easy deployment options using SQL and REST interfaces.
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.
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 post
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.
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.
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.
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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