AI Deployment

Table of Contents Introduction Understanding LLMs and Azure’s Role Azure Machine Learning for LLMOps The LLM Lifecycle in Azure Data Preparation and Management Model Training and Fine-Tuning Deploying LLMs on Azure Advanced Techniques: RAG and Prompt Engineering Best Practices for LLM Deployment Monitoring and Management Resources and Further Learning Conclusion Introduction Large Language Models (LLMs) have revolutionized artificial intelligence, enabling organizations to build sophisticated generative AI applications that understand and generate human-like text. However, deploying and managing LLMs at scale requires more than just powerful models—it demands robust infrastructure, careful orchestration, and operational excellence. This is where LLMOps (Large Language Model Operations) comes into play, and Azure Machine Learning provides the comprehensive platform to make it all possible. ...

Table of Contents Introduction: Why Hugging Face Matters What is Hugging Face? The Hugging Face Ecosystem Core Libraries Explained Getting Started: Your First Model Fine-Tuning Models for Custom Tasks Advanced Workflows and Pipelines Deployment and Production Integration Best Practices and Common Pitfalls Performance Optimization Tips Choosing the Right Model and Tools Top 10 Learning Resources Introduction: Why Hugging Face Matters Hugging Face has fundamentally transformed how developers and AI practitioners build, share, and deploy machine learning models. What once required months of research and deep expertise can now be accomplished in days or even hours. This platform democratizes access to state-of-the-art AI, making advanced natural language processing and computer vision capabilities available to developers of all skill levels. ...