Db2 Big SQL use case: Operationalize machine learning models

Data scientists create, train, and test machine learning (ML) models to find new opportunities. There is no simple way to operationalize it when data is disparate. Using Db2® Big SQL, you can rationalize disparate data using SQL skills and also feed in new data from siloed sources in a secure way.

Organizations understand the importance of machine learning and are exploring ways to implement it to improve their business. Your challenge is to find the best way of operationalizing machine learning for your business. Data gravity creates silos in organizations, and it’s difficult to bring all this data together for analysis.

Even if you bring the data together, using an ML model with your data requires a special set of skills and development effort. After operationalizing the ML model, you want to take actions on any insights you discovered. These actions can be of variety and demand integration and development efforts. You can't be agile and swiftly act on data unless these problems are tied together and addressed with a self-service tool.

Db2 Big SQL integrates with notebooks such as Zeppelin or DSX, enabling you to quickly model your data and not worry about how to get to the specific data set of choice. This approach to advanced analytics allows you to manipulate data to spot business opportunities using SQL, without requiring you to have a background in statistics or technology.

Benefits:

• Utilize SQL skills to operationalize the ML model
• When data is disparate, no need for data movement but rather query data from disparate sources to feed into the model
• Improves efficiency in an organization