The current state of MLOps for machine learning engineers

MLOps refers to the operation of machine learning in production. It combines DevOps with lifecycle tracking, reusable infrastructure, and reproducible environments to operationalize machine learning at scale across an entire organization. The term MLOps was first coined by Google in their paper on Machine Learning Operations, although it does have roots in software operations. Google’s goal with this paper was to introduce a new approach to developing AI products that is more agile, collaborative, and customer-centric. MLOps is an advanced form of traditional DevOps and ML/AI that mostly focuses on automation to design, manage, and optimize ML pipelines.

MLOps is based on DevOps, which is a modern practice for building, delivering, and operating corporate applications effectively. DevOps began a decade ago as a method for rival tribes of software developers (the Devs) and IT operations teams (the Ops) to interact.

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