MLOps: Deploying ViT ONNX Models on Azure ML Endpoints

Deploying modern computer vision architectures to production requires efficient model optimization and reliable cloud infrastructure. This text-based course guides you through the practical workflow of converting Vision Transformer (ViT) models to ONNX and serving them via Azure ML Managed Endpoints. You will develop a solid understanding of MLOps deployment pipelines, learning how to streamline model inference and manage production endpoints with confidence. What you'll learn: - Understand core MLOps terminology, model serving architectures, and inference lifecycles - Convert and optimize Vision Transformer (ViT) models using the ONNX runtime - Configure environments, scoring scripts, and container assets for model serving - Deploy real-time inference services on Azure ML Managed Online Endpoints - Apply basic monitoring, logging, and performance testing to production endpoints - Implement modern deployment workflows and resource management practices The course begins with foundational concepts in model serialization and cloud inference before guiding you step by step through configuration files, scoring code, and deployment commands. This course is designed for aspiring ML engineers, data practitioners, and developers seeking a clear, accessible entry point into modern machine learning operations. Start reading today to build reliable model deployment pipelines.