Scaling TinyML with MLOps: Deploying Machine Learning to Edge Devices

Deploying machine learning models to tiny, resource-constrained hardware is only half the battle; maintaining and scaling them in the wild requires robust systems. This text-based course guides you through the essential practices of Machine Learning Operations (MLOps) tailored specifically for low-power edge devices. You will transition from running isolated local models to designing automated, scalable pipelines that keep your edge deployments reliable and efficient. What you'll learn: - Understand the foundational concepts of TinyML and the core stages of the MLOps lifecycle. - Apply model optimization techniques like quantization to fit strict hardware constraints. - Configure automated deployment pipelines and basic CI/CD workflows for edge devices. - Monitor remote device performance and detect model drift in production environments. - Implement version control practices for both data and tiny machine learning models. This course begins with essential terminology and fundamental concepts of edge deployment before guiding you through structured written tutorials on automation, optimization, and monitoring. It is designed for beginners, software developers, and aspiring machine learning engineers, requiring no prior experience with hardware or advanced MLOps tools. Start building your foundation in scalable edge AI today.