Large Language Models with Azure

Master Large Language Model Operations on Azure

• Unlock Azure's full potential for deploying & optimizing Large Language Models (LLMs)
• Build robust LLM applications leveraging Azure Machine Learning & OpenAI Service
• Implement architectural patterns & GitHub Actions workflows for streamlined MLOps

Course Highlights:

• Explore Azure AI services and LLM capabilities
• Mitigate risks with foundational strategies
• Leverage Azure ML for model deployment & management
• Optimize GPU quotas for performance & cost-efficiency
• Craft advanced queries for enriched LLM interactions
• Implement Semantic Kernel for enhanced query results
• Dive into architectural patterns like RAG for scalable architectures
• Build end-to-end LLM apps using Azure services & GitHub Actions

Ideal for data professionals, AI enthusiasts & Azure users looking to harness cutting-edge language AI capabilities. Gain practical MLOps skills through tailored modules & hands-on projects.

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