Over eighty percent or more of companies that attempt to integrate machine learning into operational applications fail. How could this be? Many organizations underestimate the difficulty of implementing ML. This talk emphasizes the significance of machine learning operations (MLOps) in scaling TinyML to enterprise-scale deployments that provide real-world value. Training and deploying a machine learning model on a single tiny embedded device is one thing; it is quite another to scale to thousands of devices. TinyML adds a number of embedded ecosystem-specific impediments to the conventional machine learning deployment pipeline, hence considerably complicating ML deployment even further. To address these myriad issues, the talk introduces a seven-stage MLOps architecture for operationalizing TinyML successfully. These stages range from ML model development for a fleet of heterogeneous devices to continuous monitoring for detecting data drift and everything in-between. The framework is a comprehensive end-to-end workflow for scaling TinyML deployments from a proof of concept to a real-world solution.