Advanced Computing in the Age of AI|Tuesday, October 27, 2020
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Enterprise ML Moves Slowly Up the Value Chain 

While developers are moving up the machine-learning learning curve, a vendor survey finds they still have a way to go in scaling ML deployments.

Algorithmia’s annual survey of the state of enterprise machine learning technology released on Thursday (Dec. 12) did find an uptick in machine learning deployments over the past year. A modest 22 percent of companies surveyed report they have transitioned machine learning models to production.

As developers and data scientists roll up their sleeves, several pain points have emerged, ranging from version control and model reproducibility to “executive buy-in” and “aligning stakeholders,” the survey found. A key stumbling block is the familiar problem of scaling ML deployments. For example, the survey found that half of developers polled said they require up to 90 days deploying a single machine learning model.

Hence, Algorithmia concludes the state of enterprise machine learning is at the “fledgling but maturing” phase across most industries with software and IT vendors leading the charge. Investments in machine learning projects are growing, often by up to 25 percent over last year, with the heaviest investments coming from the banking, IT and manufacturing sectors.

“The bottom line is that we do see a shift toward greater ML maturity in all companies surveyed,” Algorithmia concludes, with the caveat that the percentage of production models will remain low. The key will be overcoming so-called “last-mile” deployment issues while boosting the sophistication of deployed models.

Read the full story here at sister web site Datanami.

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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