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Oracle Machine Learning

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What is Oracle Spatial?

In line with Oracle's mission to allow users to view data in new and creative ways while revealing deep insights, Oracle Database now includes advanced features for machine learning, spatial analysis, and graph capabilities. Users with an Oracle Database license can take advantage of these state-of-the-art tools for both development and deployment, whether in on-premise setups or through Oracle Cloud Database Services. The integration of Oracle's spatial database into the converged database architecture makes it easier for developers and analysts to launch location intelligence analytics and mapping services. This functionality significantly enhances the capabilities of Geographic Information System (GIS) professionals, enabling them to develop sophisticated geospatial applications seamlessly. Moreover, organizations gain the advantage of managing different types of geospatial data, performing a wide range of spatial analytical tasks, and leveraging dynamic map visualization capabilities through the spatial features provided in both Oracle Autonomous Database and Oracle Database. These improvements not only enhance data management efficiency but also encourage greater innovation and creativity in the field of data analytics, making it an invaluable asset for businesses aiming to harness the full potential of their data resources. Ultimately, Oracle’s latest features position it as a leader in the evolving landscape of data technology.

What is Oracle Machine Learning?

Machine learning uncovers hidden patterns and important insights within company data, ultimately providing substantial benefits to organizations. Oracle Machine Learning simplifies the creation and implementation of machine learning models for data scientists by reducing data movement, integrating AutoML capabilities, and making deployment more straightforward. This improvement enhances the productivity of both data scientists and developers while also shortening the learning curve, thanks to the intuitive Apache Zeppelin notebook technology built on open source principles. These notebooks support various programming languages such as SQL, PL/SQL, Python, and markdown tailored for Oracle Autonomous Database, allowing users to work with their preferred programming languages while developing models. In addition, a no-code interface that utilizes AutoML on the Autonomous Database makes it easier for both data scientists and non-experts to take advantage of powerful in-database algorithms for tasks such as classification and regression analysis. Moreover, data scientists enjoy a hassle-free model deployment experience through the integrated Oracle Machine Learning AutoML User Interface, facilitating a seamless transition from model development to practical application. This comprehensive strategy not only enhances operational efficiency but also makes machine learning accessible to a wider range of users within the organization, fostering a culture of data-driven decision-making. By leveraging these tools, businesses can maximize their data assets and drive innovation.