MLOps: ViViT ONNX Inference on Azure ML and AKS

Deploying complex vision models in production requires structured MLOps workflows and optimized inference pipelines. This text-based guide teaches you how to export Video Vision Transformer (ViViT) models to ONNX and serve them reliably using Azure ML Managed Endpoints and Azure Kubernetes Service (AKS). You will build a complete end-to-end understanding of modern machine learning operations, from model conversion and runtime optimization to containerized cloud deployment and monitoring. What you'll learn: - Understand core MLOps principles, model lifecycle stages, and inference architecture - Convert and optimize Video Vision Transformer architectures using the ONNX Runtime - Configure custom scoring scripts and environment definitions for containerized workloads - Deploy real-time inference services with Azure ML Managed Online Endpoints - Manage scalable Kubernetes compute clusters using AKS for high-throughput serving - Implement monitoring, logging, and health checks to maintain production reliability The course begins with fundamental terminology and ONNX basics before guiding you step by step through configuration files, deployment scripts, and operational best practices. Designed for developers, data scientists, and engineers new to model serving, this course requires no prior experience with AKS or ONNX. Start reading today to master production-grade vision model deployments.