Scaling TinyML: MLOps and Deployment for Edge Devices

Deploying machine learning models to tiny, low-power microcontrollers is one thing, but managing thousands of these edge devices in the wild is a completely different challenge. This course guides you through the core principles of Machine Learning Operations (MLOps) tailored specifically for the unique constraints of TinyML. You will transition from building isolated edge models to understanding how to deploy, monitor, and maintain them at scale. Through clear written explanations and practical conceptual exercises, you will master the workflows required to keep your tiny models running efficiently and reliably in production. What you'll learn: - Understand the foundational concepts of TinyML and the role of MLOps on resource-constrained hardware. - Design automated deployment pipelines for updating models on remote edge devices. - Configure telemetry and monitoring strategies to track model performance under tight hardware limits. - Detect data drift and model degradation in remote environments without draining device batteries. - Apply modern lifecycle management practices to update models securely over the air. The course starts with essential terminology and the foundational constraints of edge hardware before moving into practical deployment strategies and remote monitoring workflows. You will study real-world scenarios and complete written design exercises to solidify your understanding of scaling edge AI. This course is designed for aspiring edge AI engineers, developers, and technology enthusiasts who want to learn how to scale micro-ML models, with no advanced background in hardware or operations required. Begin your journey into the world of scalable edge intelligence today.