Benefits of In-Database Machine Learning

Oracle Machine Learning in Oracle 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.

OML within Oracle Database offers the following advantages:

• No Data Movement: Some machine learning products require that the data be exported from a corporate database and converted to a specialized format. With OML, no data movement or conversion is needed. This makes the entire process less complex, time-consuming, and error-prone, and it allows for the analysis of very large data sets.

• Security: Your data is protected by the extensive security mechanisms of Oracle Database. Moreover, specific database privileges are needed for different machine learning activities. Only users with the appropriate privileges can define, manipulate, or apply machine learning model objects, and get access to in-database and third-party models, and R and Python objects and scripts. In-database machine learning models are fully integrated objects within Oracle Database. They are created directly within the database and can be used immediately within the database environment.

• Data Preparation: Most data must be cleansed, filtered, normalized, sampled, and transformed in various ways before it can be mined. Up to 80% of the effort in a machine learning project is often devoted to data preparation. OML can automatically manage key steps in the data preparation process.

• No-code User Interfaces : No-code AutoML user interfaces, Data Monitor, Model Monitor, and the model's UI for model deployment, improve data scientist productivity and give non-experts access to powerful in-database classification and regression techniques.

• Ease of Data Refresh: Machine learning processes within Oracle Database have ready access to refreshed data. OML can easily deliver machine learning results based on current data, thereby maximizing its timeliness and relevance.

• Platform for advanced analytics: Oracle Database provides powerful features for advanced analytics and business intelligence, enabling seamless integration of machine learning with other analytics capabilities, such as statistical analysis, graph processing, spatial analysis, and analytic views: all within the same environment. This converged setup allows for more efficient, in-depth insights without the need to move data across different systems, enhancing performance and simplifying data management.

• Oracle Technology Stack: You can take advantage of the broader Oracle technology stack to integrate machine learning within a larger framework for business intelligence or scientific inquiry.

• Application Programming Interfaces: The APIs for SQL, R, Python, and REST along with SQL language operators, provide direct access to OML functionality in Oracle Database.