ClearML, a leading open source, end-to-end MLOps platform, announced wide availability of its new, 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).
“I think that in the last 5 years we have seen an immense growth of AI and ML, from academic proof of concepts to purposely built products reaching general availability,” said Moses Guttmann, CEO and co-founder of ClearML. “With the increase in efforts of democratizing AI and ML, we are seeing more companies adopt ML as part of their internal R&D efforts. I believe that this momentum will continue, picking up pace even in a down economy. Buzzy topics like ChatGPT are driving more interest and adoption in these solutions and technologies.”
The new report includes critical new statistics and findings, including:
MLOps Has Gone Mainstream
Machine learning operations streamlines, continuously orchestrates, and automates machine learning model development, deployment, and governance, enabling the commercialization of machine learning at scale. ClearML’s study found that MLOps has achieved wide scale adoption within companies and enterprises, as 85% of respondents said they had a dedicated MLOps budget in 2022, while 14% said they did not have budgets in place but expected they would in 2023.
“As organizations invest billions of dollars into machine learning, MLOps enables businesses to automate and orchestrate the entire ML workflow more efficiently and effectively,” said Guttmann. “This includes everything from experiment, development, and deployment, to management, observability, and governance of ML. MLOps empowers them to achieve better performance from their models, reach ML operationalization more rapidly, and realize commercial value, ensuring ROI for growing ML investments. Businesses want to see their ML investments materialize – and automation is key to gaining and maintaining scale and competitive edge, especially in times when resources are limited by challenging market conditions.”
MLOps Investments to Surge in 2023
Virtually all respondents said their organizations plan to increase MLOps investments in 2023, with 42% saying spend would rise 11-25%, followed by 26-50% (37% of respondents), 51-75% (16% of respondents), 76-100% (5% of respondents), and 10% or less (2% of respondents). Ultimately, 98% of respondents plan to increase investments by 11% or more in the New Year, with the overwhelming majority – nearly 60% (58%) – saying they would boost spend by over 25%. In comparison, Gartner predicts IT-related spending overall will grow by just 5.1% in 2023.*
“Companies that wrestle with integrating ML applications with their existing production applications waste invaluable resources on machine learning projects that never see the light of day or put into production. Machine learning product innovation is increasingly relied on to drive critical business value and revenue for companies, especially now,” said Guttmann. “Unified MLOps platforms provide the continuous orchestration and automation of the entire ML lifecycle process that makes this possible, by treating ML models as reusable software artifacts. Companies can seamlessly deploy, monitor, and retrain models in a repeatable process helping them derive business value and innovation from their data better and faster.”
Sign up for the free insideBIGDATA newsletter.
Join us on Twitter: https://twitter.com/InsideBigData1
Join us on LinkedIn: https://www.linkedin.com/company/insidebigdata/
Join us on Facebook: https://www.facebook.com/insideBIGDATANOW