MLOps: In-depth Guide to Benefits, Examples & Tools for 2023

Building machine learning models and applying them to business processes requires collaboration between data scientists, data engineers, designers, business professionals, and IT professionals. Efficient collaboration and orchestration is especially critical for businesses that want to adopt AI and ML at scale, which leads to a three-fold increase in ROI over companies in the AI proof-of-concept stage. Inspired by DevOps practices for software development, MLOps brings diverse teams in an organization together to speed up the development and deployment of machine learning models. In this article, we'll provide an in-depth guide to MLOps, how it helps streamline end-to-end ML processes, and some case studies from companies who have adopted it. MLOps (Machine Learning Operations) is a set of practices to standardize and streamline the process of construction and deployment of machine learning systems.