4 What is In-Database Machine Learning

Oracle Machine Learning (OML) provides scalable in-database machine learning algorithms through PL/SQL, SQL, Python, and R APIs. OML has over 30 scalable machine learning algorithms directly in the database, which helps you develop and deploy solutions quickly for applications and dashboards.

OML eliminates costly and risky data movement for database data. By avoiding separate analytical engines, you simplify your solution architecture, as there’s no need to manage and test workflows involving remote third-party engines. OML algorithms support algorithm-specific automatic data preparation and individual prediction details, with scalable batch and real-time scoring (inferencing). OML is included with Autonomous AI Database instances and Oracle AI Database licenses.

• Overview of In-Database Machine Learning
  OML provides a powerful, state-of-the-art machine learning capability within Oracle AI Database. The parallelized algorithms in the database keep data under database control. There is no need to extract data to separate machine learning engines, which adds latency to data access and raises concerns about data security, storage, and recency.
• Benefits of In-Database Machine Learning
  Oracle Machine Learning in Oracle AI Database securely enables data scientists and non-experts to easily build accurate models without moving data, automating data preparation, leveraging no-code interfaces, APIs, and integrated analytics features.
• Features of In-Database Algorithms
  Oracle Machine Learning offers a suite of tools enhancing productivity for data scientists, developers, and data engineers. This suite streamlines machine learning model development, evaluation, and deployment, catering to both experts and non-experts in the field.
• Optimization features of Oracle Exadata and Oracle RAC
  Oracle Exadata and Oracle RAC offer advanced optimization features for machine learning by leveraging distributed parallelism, storage-tier processing, and dynamic resource allocation to enable scalability, high-performance model building, and real-time scoring.