Training ML Models and exporting it in more optimized way for Edge device from scratch is quite challenging thing to do especially for a beginner in ML space. Interestingly Azure Cognitive Services will aid in heavy lifting half of the common problems such as Image Classification, Speech Recognition etc. So in this article, I will show you how I created a simple pipeline(kind of MLOps) that deploys the model to an Edge Device leveraging Azure IoT Modules and Azure DevOps Services. Blob Storage – For storing images for ML training 2. Logic Apps – To respond Blob storage upload events and trigger a Post REST API call to Azure Pipelines 3. Cognitive Services – For training Images and generate a optimized model specifically for edge devices. Containerized Az Devops Agents will be running inside this, orchestrated using K3s Kubernetes Distribution.