BERT to ONNX Model Export and C# Inference for LLMOps

Deploying transformer models into production requires efficient runtime environments and reliable deployment pipelines. This text-based guide teaches you how to export BERT models to the open ONNX format and integrate them directly into C# and .NET applications for high-performance inference. You will start with core terminology, foundational machine learning deployment concepts, and tokenization principles before diving into hands-on implementation. By following clear explanations and written code examples, you will understand the complete lifecycle of exporting, optimizing, and running natural language processing models outside of traditional Python environments. What you'll learn: - Understand core LLMOps concepts, model serialization, and the ONNX open format ecosystem - Export pretrained BERT models from PyTorch or Hugging Face to optimized ONNX graphs - Implement text tokenization workflows in C# using tokenizer libraries - Configure ONNX Runtime inside .NET applications for fast CPU and GPU inference - Apply post-processing routines to extract embeddings and classification outputs in C# - Practice troubleshooting common tensor dimension mismatches and runtime errors You will begin with essential architecture basics and format specifications before progressing step-by-step through model conversion, token handling, and production-ready C# execution patterns. This course is designed for software developers and data enthusiasts who want to integrate natural language models into .NET environments without prior MLOps background. Start reading today to build reliable, high-performance model pipelines in C#.