Machine Learning’s Value Threatened by Challenges to Operationalizing Models, Survey Finds
A first look at ClearML’s new report, “MLOps in 2023,” also finds that nearly one-third (29%) of respondents say a ‘lack of talent’ is a key challenge in operationalizing ML at scale.
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ClearML, provider of a unified, end-to-end MLOps platform, announced initial findings from its in-depth research report, MLOps in 2023: What Does the Future Hold? Polling 200 U.S.-based machine learning decision makers, the report examines key trends, opportunities, and challenges in machine learning and MLOps (machine learning operations).
The full report will be released in the coming weeks. Initial findings are being reported now.
Challenges in Operationalizing ML Undercut Opportunities
When asked if their organizations were challenged to create business and commercial value from ML investments -- by deploying or productizing machine learning pipelines and projects at scale -- 86 percent agreed, with almost one-third (29 percent) saying they were “very challenged.”
Similarly, 71 percent said their company was missing out on revenue or value creation due to challenges in operationalizing ML at scale. Almost half of all respondents (45 percent) described these challenges as either “severe” or “very severe.”
“[Although] machine learning can bring a great deal of value to organizations across every vertical, it’s important to recognize that businesses can only actualize that value when they operationalize the ML model,” said Moses Guttmann, CEO and co-founder of ClearML. “Unfortunately, from model development to deployment to monitoring, respondents say operationalizing ML is increasingly challenging. For that reason, we are confident that MLOps will continue to see greater adoption to address these issues, enhancing collaboration, project management, and operationalization.”
Under Layoff Crunch, Data Science Teams Forced to Do More With Less
In recent weeks, Twitter, Meta, Amazon, and several other large tech companies have announced layoffs and hiring freezes amid growing macroeconomic concerns. With that in mind, when asked to name the biggest challenge in operationalizing ML at scale within their organizations, one of the most popular responses was a “lack of talent,” according to nearly one-third (29 percent) of those polled.
“Even before the massive layoffs and the shift in the economy, ML and data science teams found it hard to operationalize ML at scale and drive value due to shortage of talent,” Guttmann explained. “The layoffs will only further amplify their pain. Now they'll have to do more with less in a time where they need more headcount. This will drive companies to adopt more automation and MLOps tools that can help them bridge the gaps.”