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

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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

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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

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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