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ML/DL

they will create holistic health plans based on data constantly updated from activity trackers, eating patterns and medical tests.

Even with the most advanced techniques, data scientists spend countless hours developing, testing and retooling analytic models one step at a time.

Instead of simply helping data scientists crunch the numbers, organizations will use machine learning to automatically understand and learn from data.

The result will move us from the current state of predictive analytics, where organizations guess what will happen next, to cognitive business, where organizations develop a deep understanding of markets based on continuously updated data.

And automated: …the first cognitive platform for continuously creating, training and deploying a high volume of analytic models

…allows data scientists to automate the creation, training, and deployment of operational analytic models It reminds me of that scene in the first season of Mad Men, when Don Draper develops the tagline for Lucky Strike.

Smart banks stopped obsessing about churn around fifteen years ago when they learned that there is no cost-effective way to retain a customer once their behavior signals that they are at risk to leave.

The focus in CRM shifted long ago to building relationships over the long term rather than begging customers to stick around until the end of the quarter, so our bonuses don’t get hammered.

IBM tries to position this bug as a feature, arguing that mainframes are the logical platform for machine learning because they are “the operational core of global organizations where billions of daily transactions are processed…” It’s a nonsense argument.

Nobody runs machine learning directly in an operational data store: the DBAs won’t allow it, the data is crap, and we don’t want to bring down the ATMs by running a genetic algorithm.

You’re going to copy the operational data somewhere. It makes very little difference whether you copy it to another mainframe, copy it to a data warehouse appliance, or copy it to Hadoop —

Remind her that many machine learning platforms already run on commodity hardware – including actually automated platforms.

When she tries to tell you that it makes sense to put machine learning on the mainframe because it is “the operational core of global organizations where billions of daily transactions are processed…”, Ask: why?

If you happen to have a spare IBM mainframe computer sitting idle, ask the rep whether IBM Machine Learning is a product or a service offering.

Pay attention to this detail in the Gartner report: Customers expressed concerns about purchasing products from IBM, as the company reportedly often tries to bring its consulting organization, IBM Global Business Services, into data science projects.

Tell your rep that you are willing to forego cars that read your mind and shopping carts that fill themselves if you can have technical support that doesn’t suck.

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