Abstract

This IDC study evaluates vendors that offer MLOps technologies and capabilities. 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 tools, technologies, and methodologies to improve collaboration between data scientists and operational engineers, automate and accelerate model velocity, and provide for monitoring and governance of machine learning models in production.

"Top challenges that customers face with implementing machine learning initiatives in production include lack of expertise, cost, and lack of automation," said Kathy Lange, research director, AI Software research at IDC. "MLOps software and processes 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."

Coverage