Large Language Models with Azure

Week 1: Introduction to LLMOps with Azure \\- Discover pre-trained LLMs in Azure and deploy basic LLM endpoints \\- Identify strategies for mitigating risks when using LLMs \\- Explain how large language models work and their potential benefits and risks \\- Describe the core Azure services and tools for working with AI solutions like Azure ML and the Azure OpenAI Service **** Week 2: LLMs with Azure - Use Azure Machine Learning, including GPU quota management, compute resource creation, model deployment, and utilization of the inference API - Use the Azure OpenAI Service and its playground by deploying models and creating required resources - Apply your comprehension of keys, endpoints, and Python examples to integrate Azure OpenAI APIs, monitor usage, and ensure proper resource cleanup **** Week 3: Extending with Functions and Plugins - Use Semantic Kernel to create advanced, context-aware prompts for large language models - Define custom functions to extend system capabilities - Build a microservice for reusable functions to streamline system extensions - Implement functions using external APIs and microservices to customize model behavior **** Week 4: Building an End-to-End LLM application in Azure - Understand architectural patterns like RAG for building LLM applications - Use Azure AI Search to create search indexes and embeddings to power RAG - Build GitHub Actions workflows to automate testing and deployment of LLM apps - Deploy an end-to-end LLM application leveraging RAG, Azure, and GitHub Actions