As we write the book Machine Learning in Practice (coming early in 2019), we’ll be posting draft excerpts right here.
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HFS Research is out with a new study on machine learning in enterprise organizations, and the results are fascinating: while business leaders believe in the value of machine learning, their companies are not doing nearly enough to take advantage of it.
Take a look at some of the top-level findings:
Business leaders understand that ML matters:
- 86% of data science decision makers across the Global 2000 believe machine learning impacts their industries today.
- 60% believe machine learning will be a core technology within 2 years; a whopping 94% say it will happen within the next 5 years.
Businesses aren’t doing enough:
- Most enterprises have yet to make significant investments in ML (84% investing under $1M), have decentralized practices (8% have centralized ML functions), are running a few projects (65% are running 1 to 3 ML initiatives), and believe that only a fraction of their ML projects will have business impact.
- 42% already recognize they have significant skills gaps, especially in the shift from traditional IT to ML and data science.
These findings align with what we’re seeing at Infinia ML as we help companies implement machine learning:
- Sometimes action and investment take time to catch up with interest.
- There’s a huge skills gap out there — that’s why we offer ML training for business leaders, data scientists, and software engineers.
- We believe there’s a 10-year backlog to deploy ML inside large enterprises.
One surprise? 71% of respondents said machine learning is not overhyped, despite the fact that ML is teetering at the top of the most recent Gartner Hype Cycle for Emerging Technologies.
That confidence could be a testament to machine learning’s potential for practical business impact — even if though it’s worth noting that not all ML promises are created equal. Over half of respondents believe in “a growing epidemic of so-called ML ‘experts’ who are just capitalizing on industry hype.”
While machine learning may be hyped, it’s apparently not overhyped. And that’s something to get hyped about.
There’s a lot more insight in the full report. What surprises you the most?