Designing Scalable ML Model Serving Architectures

Moving a machine learning model from a local environment to a reliable production system requires a deep understanding of infrastructure and system design. Choosing the wrong serving strategy can lead to high latency, runaway cloud costs, or stale predictions. This text-based course guides you through the foundational concepts of ML serving, helping you evaluate the trade-offs between different architectural patterns. You will gain the decision-making skills needed to deploy models that are fast, cost-effective, and highly scalable. What you'll learn: - Understand the core terminology of ML systems, including latency, throughput, and data freshness. - Compare synchronous APIs using FastAPI or gRPC with asynchronous queue-based workflows. - Analyze streaming architectures for real-time, event-driven model predictions. - Apply cost-optimization strategies and resource allocation principles for containerized model deployments. - Evaluate modern serving patterns, including serverless inference and basic model caching strategies. We begin with the fundamental definitions of model serving before diving deep into architectural patterns, design scenarios, and trade-off analysis. You will read structured explanations and analyze architectural blueprints to build a robust mental model of production MLOps. This course is designed for software engineers, data scientists, and aspiring MLOps professionals who understand basic machine learning concepts but are new to production system architecture. No prior experience with systems engineering is required. Start building resilient, production-ready machine learning systems today.