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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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.
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.
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.
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.
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.
Improve data scientist productivity and help nonexperts use powerful in-database algorithms for classification and regression through a no-code AutoML user interface.
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.
Achieve immediate machine learning model availability with easy deployment options using SQL and REST interfaces.
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.
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.
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.
Oracle 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 postWith 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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