C# Image Recognition with ResNet50v2 and MLOps Fundamentals

Deploying machine learning models into reliable production applications requires both robust software engineering and disciplined operations. This course guides you through applying computer vision techniques in C# by leveraging the pre-trained ResNet50v2 architecture and establishing practical MLOps workflows. You will start with core machine learning definitions, image processing fundamentals, and model serving concepts before learning how to run inferences and operationalize computer vision pipelines. What you'll learn: Understand fundamental concepts of deep learning, image recognition, and MLOps lifecycles. Configure and load the ResNet50v2 model using modern C# and ONNX runtime environments. Preprocess image inputs and interpret inference predictions programmatically. Implement model versioning, tracking, and continuous integration workflows for machine learning assets. Containerize C# inference services for reproducible deployments across environments. Monitor model performance and manage data drift in production environments. The text-based material moves sequentially from fundamental theory and terminology to practical code implementations and operational patterns. This guide is designed for developers and beginners eager to bridge software engineering with machine learning operations, requiring no prior deep learning background. Start reading today to build and maintain production-ready image recognition systems in C#.