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IBM Machine Learning for z/OS Gives Clients the Tools to Make Better, Faster Decisions

IBM Machine Learning for z/OS
Illustration by Paul Farrington / StudioTonne

These are the types of data analysis puzzles IBM Machine Learning has been designed to help solve.

Model Making—and Remaking

One of the capabilities offered by IBM Machine Learning for z/OS is the Cognitive Assistant for Data Scientists. This IBM-patented technology helps eliminate the trial-and-error process so data scientists can consistently and more easily choose the best model to address business problems.

A second key IBM Machine Learning capability is Hyper Parameter Optimization (HPO). “At times, a data scientist can have as many as 100 parameters to consider,” Jacopi says. “This makes it time-consuming and difficult to determine which are the most critical parameters to optimize the model and gain the best outcome.” HPO aids the data scientist in selecting the best parameters to optimize the predictive capabilities of the model.

Once the model has been tested to ascertain that it’s providing useful, accurate predictions, putting that model to work “is a push-button deployment,” Jacopi says. Clients have told her it can take anywhere from three to six months to deploy a model using traditional methods. With IBM Machine Learning for z/OS, models can now be deployed in seconds. If the model needs to be updated, that new iteration also can be instantly deployed. As a result, she adds, the data scientist can “address business problems faster and deliver greater value.”

In addition, IBM Machine Learning for z/OS incorporates and employs a continuous monitoring and feedback loop. As new data arises, a data scientist must determine whether the model developed months ago is still providing valid predictions. IBM Machine Learning for z/OS includes a dashboard that provides a real-time view of model accuracy. When deploying a model, a data scientist can set a threshold (e.g., if the model degrades below 90 percent). IBM Machine Learning for z/OS will notify the data scientist if and when the model accuracy falls below this level. He or she then can decide whether to refresh the model with the new data to improve accuracy.

IBM Machine Learning for z/OS also lets application developers use RESTful APIs to “call” a behavioral model to get a prediction. For example, if the business is struggling with fraudulent credit card transactions, a data scientist using IBM Machine Learning for z/OS can develop a credit card fraud detection model and deploy it. “RESTful APIs make it simpler for the application to leverage the output from the model,” says Jacopi.

New Insights, New Opportunities

Organizations can use machine learning-generated models to make operational improvements. One of Jacopi’s clients, a healthcare company, has been seeking ways to help patients improve their health. The client has a great deal of data on each of its customers, including lists of prescribed medications and whether these patients take their medications regularly. Based on this data and other information, the company can use machine learning to better understand—and thus improve—each customer’s distinctive health behaviors.

“It’s looking for better ways to pull together large volumes of complex data, make better recommendations and improve client health,” Jacopi explains. “They want to help their customers make better choices and positively influence their own healthcare. In this scenario, patients lead healthier lives and costs are lower, so everyone wins.”

Yes, digital data can be overwhelming. But data is also highly valuable, and it’s crucial to an organization’s success in the digital age. Big data not only allows enterprises of all kinds to better understand their customers—it can also help them better engage with and serve them.

“With IBM Machine Learning for z/OS, data scientists will be even more productive and valuable to the organization,” Jacopi says. “Using machine learning, data scientists can uncover new insights from their data to support better outcomes. These innovative insights will generate new business opportunities.”

Gene Rebeck is a freelance writer based in Duluth, Minnesota.

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