Deploying Fine-Tuned ViT Classifiers in C# on CPU

Deploying deep learning vision models directly into production .NET applications can seem daunting when balancing performance and infrastructure costs. This text-based guide demonstrates how to take fine-tuned Vision Transformer (ViT) models and run high-efficiency CPU inference using C# without relying on expensive GPU clusters. You will build a solid understanding of how transformer architectures handle image data and how to integrate trained weights into a standard .NET backend pipeline. Through practical written explanations and clear code examples, you will explore model optimization, tensor transformations, and reliable MLOps deployment workflows. What you'll learn: - Understand foundational Vision Transformer concepts, terminology, and inference pipelines - Convert and optimize fine-tuned ViT models using standard formats such as ONNX for CPU deployment - Implement image preprocessing, tokenization, and tensor reshaping directly in C# - Configure high-performance runtime inference engines within .NET applications - Apply basic MLOps monitoring and logging practices to track inference latency and accuracy - Practice structuring clean, testable C# services for production-ready model execution The course begins with foundational machine learning and transformer concepts before moving step-by-step through image preprocessing, runtime execution in C#, and production deployment strategies. This course is designed for .NET developers and machine learning beginners looking to bridge the gap between trained computer vision models and practical C# software architecture. No prior deep learning deployment experience is required. Start reading today to bring modern Vision Transformer inference into your C# applications.