MLOps: Scalable ML Inference with Distributed Messaging

Serving machine learning models in production requires robust architectures that handle fluctuating traffic loads without service degradation. This text-based course guides you through decoupling model inference workloads using proven distributed messaging patterns. You will build a solid understanding of how message brokers handle incoming prediction requests, balance workloads across worker pools, and maintain low latency under heavy throughput. Through clear written explanations and structured implementation examples, you will learn to calculate resource requirements and size your inference infrastructure effectively. What you'll learn: - Understand core MLOps terminology, inference paradigms, and distributed system concepts - Design asynchronous and event-driven architectures for decoupled model serving - Configure message brokers and queues to manage prediction workloads reliably - Dimension worker pools, compute resources, and batch sizes based on real-world throughput - Apply modern observability practices to monitor queue lag, throughput, and inference latency - Implement error handling, dead-letter queues, and retry mechanisms for fault tolerance The course begins with foundational definitions and queuing principles before advancing through architectural blueprints and dimensioning techniques. Designed for software developers, data practitioners, and aspiring MLOps engineers, this course requires only basic programming familiarity. Start reading today to build scalable, production-ready machine learning inference pipelines.