Deploying AI Models in Azure Cloud

Based on the project experiences working on AI (Artificial Intelligence) & ML (Machine Learning) projects with AML (Azure Machine Learning) platform since 2018 in this article we will share a point of view (the good parts) on bringing your AI models to production in Azure Cloud via MLOps. It is a typical situation when the initial experimentation (a trial and error approach) and the associated feasibility study to produce a certain model takes place in AML/Jupyter Notebook(s) first. Once some promising results have been obtained, analyzed and validated via the feasibility study by the Machine Learning Engineering team "locally", the Application Engineering and DevOps Engineering teams can collaborate to "productionalize" the workload at scale in the Cloud (and/or at the Edge as needed). AML (Azure Machine Learning) is an MLOps-enabled Azure's end-to-end Machine Learning platform for building and deploying models in Azure Cloud. Please find more information about Azure Machine Learning (ML as a Service) here, and more holistically on Microsoft's AI/ML reference architectures and best practices here. AML Python SDK is great for development and automation, AML CLI is more convenient for Dev(Sec)Ops/MLOps automation, and AML REST API is a lower level interface which is typically not used on projects in favor of the former 2 approaches.