LLMOps: CPU Inference for Phi-3 Vision with C#

Large language models offer immense potential, but deploying them efficiently, especially on standard CPU hardware, can be a significant challenge. This course guides you through the essential techniques and best practices for running powerful models like Phi-3 Vision without relying on expensive GPUs. By the end of this course, you will possess the foundational skills to understand, optimize, and deploy modern multimodal AI models for CPU inference, making advanced AI more accessible for your projects. What you'll learn: * Understand the core concepts of Large Language Models (LLMs) and the LLMOps lifecycle. * Learn techniques for optimizing and performing inference with LLMs on CPU hardware. * Implement model quantization, specifically 4-bit, to reduce model size and improve performance. * Explore the ONNX format for efficient model representation and cross-platform deployment. * Deploy and interact with multimodal models like Phi-3 Vision using C#. * Practice foundational MLOps principles for managing and serving models reliably. * Configure efficient inference pipelines tailored for resource-constrained environments. This course begins with fundamental LLM concepts and progresses to practical CPU inference strategies, model optimization techniques, and hands-on C# implementation for deployment. It concludes with an overview of modern deployment patterns. This course is designed for beginners interested in deploying large language models efficiently on CPU hardware. No prior experience with LLMs or MLOps is required. Start your journey into accessible and efficient AI deployment today.