TinyML aims to enhance paradigms like healthcare, surveillance, and activity detection, etc. by scaling down Machine Learning (ML) algorithms to the level of resource-constrained devices such as microcontrollers (MCUs). MLOps practices for deploying, monitoring, and updating ML models in production, which can be challenging due to the limitations of MCU devices. Therefore, this paper highlights various key challenges for the successful training, deployment, and monitoring of ML models on MCUs and their limitations from both hardware and software perspectives. Such difficulties have an impact on the productivity, dependability, and scalability of TinyML systems.