ClearML, the open supply, end-to-end MLOps platform, launched the ultimate set of information to finish its just lately launched analysis report, MLOps in 2023: What Does the Future Maintain? Polling 200 U.S.-based machine studying resolution makers, the report examines key tendencies, alternatives, and challenges in machine studying and MLOps (machine studying operations).
When requested to share the largest ache factors about their MLOps platform, instruments, or stack, 41% cited friction in utilizing instruments with different expertise. Almost one-quarter (22%) cited vendor lock – issue switching to a special supplier with out vital prices, time, or disruptions – as their greatest problem.
“MLOps as a brand new and rising area is at present dominated by fragmented level options providing a fraction of the performance corporations want for steady ML,” says Moses Guttmann, CEO and Co-founder of ClearML. “This case wants to alter. The objective needs to be to cut back fragmentation and supply extra complete options that handle all of the wants of MLOps, with a view to decrease the challenges confronted by ML practitioners and unlock billions of {dollars} in income potential for AI and ML expertise.”
Further ache factors reported by survey respondents included: value being too costly (39%), onboarding being too lengthy (35%), and the group failing to make use of the answer they paid for (14%). Additionally, 16% or respondents mentioned they don’t use third-party instruments in any respect, as a substitute opting to make use of instruments they constructed internally.
“Constructing MLOps instruments internally requires devoted expertise, expertise and capital at appreciable scale and can be extremely troublesome to maintain and keep over time,” mentioned Guttmann. “On this market, the higher possibility is to outsource to a trusted third occasion.”
Further findings embrace that an awesome majority of respondents (92%) would like to make use of one, unified MLOps platform that does the whole lot versus utilizing a number of semi-platforms and level options as a part of an MLOps stack.
“ML decision-makers are poised to extend funding in MLOps this yr, however in keeping with our survey outcomes, they’re looking for a unified end-to-end platform, not scattering spend throughout a number of level options,” says Guttmann. “With rising curiosity in materializing enterprise worth from AI and ML investments, we count on that the demand for seamless, all-in-one expertise will drive MLOps adoption.”
Click on the hyperlink to learn ClearML’s new analysis report, MLOps in 2023: What Does the Future Maintain? in full.
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