Moving a machine learning model from a local environment to a reliable production ecosystem requires shifting from a data science mindset to a robust software engineering discipline. This session breaks down the two most critical pillars of production ML: Scaling and Drift.

First, we will explore Model Scaling, focusing on how to serve models efficiently at scale using modern frameworks like FastAPI. We will cover infrastructure strategies for handling real-time vs. batch inference and optimizing resource utilization under heavy traffic. Second, we will dive into Model Monitoring and Drift, examining how to detect data and concept drift before they cause silent model degradation. Attendees will walk away with a practical blueprint for maintaining model reliability, scaling infrastructure, and ensuring long-term predictive accuracy in real-world systems.