ClearML Study: Friction a Key Challenge for MLOps Tools

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ClearML, the open source, end-to-end MLOps platform, released the final set of data to complete its recently released research report, MLOps in 2023: What Does the Future HoldPolling 200 U.S.-based machine learning decision makers, the report examines key trends, opportunities, and challenges in machine learning and MLOps (machine learning operations). 

When asked to share the biggest pain points about their MLOps platform, tools, or stack, 41% cited friction in using tools with other technology. Nearly one-quarter (22%) cited vendor lock – difficulty switching to a different provider without significant costs, time, or disruptions – as their biggest challenge.

“MLOps as a new and emerging field is currently dominated by fragmented point solutions offering a fraction of the functionality companies need for continuous ML,” says Moses Guttmann, CEO and Co-founder of ClearML. “This situation needs to change. The goal should be to reduce fragmentation and provide more comprehensive solutions that address all the needs of MLOps, in order to minimize the challenges faced by ML practitioners and unlock billions of dollars in revenue potential for AI and ML technology.”

Additional pain points reported by survey respondents included: price being too expensive (39%), onboarding being too long (35%), and the team failing to use the solution they paid for (14%). Also, 16% or respondents said they don’t use third-party tools at all, instead opting to use tools they built internally. 

“Building MLOps tools internally requires dedicated talent, technology and capital at considerable scale and will be incredibly difficult to sustain and maintain over time,” said Guttmann. “In this market, the better option is to outsource to a trusted third party.” 

Additional findings include that an overwhelming majority of respondents (92%) would prefer to use one, unified MLOps platform that does everything versus using multiple semi-platforms and point solutions as part of an MLOps stack.

“ML decision-makers are poised to increase investment in MLOps this year, but according to our survey results, they’re seeking a unified end-to-end platform, not scattering spend across multiple point solutions,” says Guttmann. “With growing interest in materializing business value from AI and ML investments, we expect that the demand for seamless, all-in-one technology will drive MLOps adoption.”

Click the link to read ClearML’s new research report, MLOps in 2023: What Does the Future Hold? in full. 

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