MLOps — Where ML Meets DevOps

MLOps — Where ML Meets DevOps

This IDC Perspective draws parallels between MLOps and DevOps approaches. As customers are moving more models from experimentation to production, they need scalable ways to collaborate, operate, and operationalize machine learning models. To leverage the tremendous opportunities that this provides, IDC recommends leveraging MLOps methodologies, building upon DevOps processes to improve collaboration between data scientists and operational engineers, automate, and accelerate model velocity."Top challenges that customers face with implementing AI/ML initiatives in production include lack of expertise, cost, and lack of automation," said Sriram Subramanian, research director, AI and Automation Software research at IDC. "MLOps capabilities enable customers to overcome these challenges by improving collaboration between data scientists, application developers, and operational engineers; automating end-to-end model life-cycle management; and increasing model velocity."

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