With the growing volume of large data sets, low-cost data storage, and more powerful data processing capabilities, the applications of machine learning (ML) are growing exponentially.
But many organizations are still reluctant to take the ML plunge. And, for those that do, the success rate for ML projects is worringly low. (Gartner notes that up to 85% of ML projects ultimately fail to deliver on their intended promises to business.)
So, what can companies do to achieve a higher success rate and benefit from the potential of ML? And, perhaps more importantly, what specific common causes of ML failures can they avoid?
We break it down here in the infographic, 6 Traps to Avoid When Implementing Machine Learning.
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