6 ways to make machine learning fail

When learning, machine learning will make mistakes. Adopters need to anticipate that—and be careful not to make matters worse through human mistakes by IT and business

The process of learning in general often means making mistakes and taking the wrong paths, and then figuring out how to avoid these pitfalls in the future. Machine learning is no different.

As you implement machine learning in your enterprise, be careful: Some of technology marketing might suggest that the learning is very right very fast, an unrealistic expectation for the technology. But the truth is that there are bound to be mistakes in the machine learning process. And these mistakes can get encoded, at least for a while, in business processes. The result: Those mistakes now happen at scale and often outside immediate human control.

“Eagerness without due diligence can lead to problems that render the benefits of machine learning almost useless,” says Ray Johnson, chief data scientist at SPR Consulting.

Detecting machine learning errors and dealing with them will help you have more success with the technology and meet your machine learning expectations.

Following are some of the issues that can increase and prolong the mistakes that machine learning tools make while they are learning—bad lessons they may never recognize and correct.

Lack of business understanding of the problem makes machine leaning fail

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