In keeping with Oracle’s mission to help people see data in new ways, discover insights, unlock endless possibilities, customers wishing to use the Machine Learning, Spatial, and Graph features of Oracle Database are no longer required to purchase additional licenses.
Large and small businesses alike know the value of applying machine learning technology to solve key business problems and achieve competitive, if not leadership, position in their industries. To democratize machine learning across the enterprise and further the ability of businesses to leverage powerful machine learning tools, Oracle Database now includes all functionality from the Oracle Advanced Analytics option as part of its base license the in-database Oracle Machine Learning APIs and functionality for SQL, R and, coming soon, Python. This applies to Oracle Database versions currently supporting Oracle Machine Learning, with future support to include Oracle Database SE2. See our broader announcement as well as specific Spatial and Graph blogs.
Move the algorithms, not the data!
With Oracle Machine Learning, Oracle moves the algorithms to the data. Oracle runs machine learning within the database, where the data reside. This approach minimizes or eliminates data movement, achieves scalability, preserves data security, and accelerates time-to-model deployment. Oracle delivers parallelized in-database implementations of machine learning algorithms and integration with the leading open source environments R and Python. Oracle Machine Learning delivers the performance, scalability, and automation required by enterprise-scale data science projects – both on-premises and in the Cloud.
Oracle Database - the multi-model converged database
Users shouldn’t have to create and manage multiple databases to access different analytical functionality, which adds complexity and cost. Instead, all such functionality should exist in a single converged, multi-model database, bringing together a broad set of algorithms that can operate on data with various data types and data models. This is a key differentiator for Oracle Database, and reinforces Oracle’s goal to provide such advanced development tools to the widest range of developers.
Empower enterprise users with SQL, R, and Python
Different business problems and their underlying data require different analytical techniques and algorithms to be successful. Oracle Database includes over 30 such algorithms. Oracle Machine Learning provides natural interfaces for the key data science languages: SQL, R, and Python. With the SQL API, in-database models are first class database objects with many of the same data management features available for other database objects like tables and views.
By supporting R and Python, data scientists and other R and Python users access Oracle Database as a high performance compute engine for scalable data exploration and preparation with native R and Python functions and syntax. The in-database algorithms come with a natural R and Python interface as well.
Production deployment - the critical step
Some might refer to production deployment as the Achilles' heal of data science projects. When application developers or IT try to integrate machine learning models or open source R and Python scripts in production, they are faced with the realities of addressing backup, recovery, security, and scalability concerns. Providing an integrated machine learning platform, like Oracle Machine Learning, deployment is immediate – SQL-derived models exist in the database and can be invoked from SQL queries, R and Python scripts can be stored in Oracle Database and executed in engines spawned and controlled by Oracle Database. Oracle greatly simplifies production deployment by providing the “plumbing” so enterprise teams can focus on the machine learning solution.
Machine learning can be applied to a wide range of business problems ranging from healthcare fraud detection to manufacturing root cause analysis, from retail product recommendation to equipment remaining useful life prediction. As enterprises amass greater data volumes and supplement corporate data with external data sources, the ability to integrate and prepare data at scale for machine learning is essential. Further, the ability to streamline the time from business problem definition to solution deployment is critical to deliver ROI on data science projects. Oracle Machine Learning customers have achieved impressive results, including:
For more details about these and other customer stories see OML Customers.
Check out this video on Introduction to Oracle Machine Learning Notebooks.
Here are a few highlights of Oracle Machine Learning functionality:
By including Oracle Machine Learning with Oracle Database on-premises and in the Cloud, Oracle continues to support a next-generation converged data management and machine learning platform.