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Evolution of Experimental AI to Essential AI with MLOps | Cypher 2022
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By Jatindra Singh Deo MLOps Implementation at Genpact, Megha Sinha MLOps GTM Leader at Genpact
Across industries, enterprise leaders are ramping up digital initiatives at an unprecedented pace, reinventing how we live and work. But as these projects get underway, it has been witnessed that roughly only half of all AI proofs of concept ever make it to production. The complexity of ML implementation varies with increasing degrees of expectation from ML infrastructure, model complexity, and deployment. At its core, machine learning operations (MLOps) help teams consistently develop, deploy, monitor, and scale AI and ML models, mitigating the potential risks associated with not having a framework for sustainable innovation.
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Get in touch with us:
Across industries, enterprise leaders are ramping up digital initiatives at an unprecedented pace, reinventing how we live and work. But as these projects get underway, it has been witnessed that roughly only half of all AI proofs of concept ever make it to production. The complexity of ML implementation varies with increasing degrees of expectation from ML infrastructure, model complexity, and deployment. At its core, machine learning operations (MLOps) help teams consistently develop, deploy, monitor, and scale AI and ML models, mitigating the potential risks associated with not having a framework for sustainable innovation.
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Get in touch with us:
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