Machine learning is the new battle cry for the cloud world. Until cloud computing came along, machine learning was out of reach for most enterprise IT shops. But now that it’s in the cloud as a service, it’s affordable. It should be no surprise that Amazon Web Services, Google, IBM, and Microsoft offer machine learning services in their clouds.
Machine learning is valuable only for use cases that benefit from dynamic learning — and there are not many of those. Examples of machine learning use cases include financial systems that deal with risk, medical diagnosis, or recommendation systems like those at Amazon.com.
But the online transaction processing (OLTP) style of applications that run most businesses are not a good fit for machine learning.
I’ve used machine learning for applications that needed to learn as they processed data, such as to recommend products or services from a website by observing the behavior of the person driving the browser. Over time, the application builds up knowledge that allows it to make good guesses about the user. In essence, it simulates humans’ ability to dynamically learn through experiences.
The problem is if you have a hammer, everything looks like a nail. Vendors pushing machine learning cloud services say it’s a good fit for many applications that shouldn’t use it at all. As a result, the technology will be overapplied and misused, wasting enterprise resources.
Watch out for machine learning fever at your business — use it where it truly makes sense, but avoid it elsewhere. You have too much necessary work to do to spend resources on unnecessary work.
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