Deploying AI Models at Scale: Challenges and Solutions

Moving an AI model from a research notebook to a production environment that can serve millions of users is a significant engineering challenge. This article provides a high-level overview of the key considerations for deploying AI models at scale. We introduce the field of MLOps (Machine Learning Operations), which brings DevOps principles to the machine learning lifecycle. We also discuss critical topics such as model optimization for lower latency and cost, choosing the right infrastructure for serving, and the importance of monitoring and maintaining models once they are in production. This is an essential read for anyone looking to build real-world AI applications.