Discover the power of MLOps: streamline ML model deployment, automate workflows, ensure reliability, and scale AI success efficiently.
Machine Learning Operations (MLOps) is a set of practices that aims to deploy and maintain Machine Learning (ML) models in production reliably and efficiently. Drawing inspiration from DevOps principles, MLOps applies similar concepts to the entire AI model lifecycle, from data gathering and model training to deployment and monitoring. The primary goal is to automate and streamline the processes involved in taking an ML model from a research prototype to a robust, scalable production application. This ensures that models not only perform well initially but also remain effective over time as new data becomes available.
MLOps practices are essential for managing complex ML systems in production environments.
A variety of tools support different stages of the MLOps lifecycle, enabling teams to build efficient and scalable workflows.