Implement Generative AI engineering with Azure Databricks

Outline

• Get started with language models in Azure Databricks
  • Understand Generative AI
  • Understand Large Language Models (LLMs)
  • Identify key components of LLM applications
  • Use LLMs for Natural Language Processing (NLP) tasks
  • Exercise - Explore language models
  • Module assessment
• Implement Retrieval Augmented Generation (RAG) with Azure Databricks
  • Explore the main concepts of a RAG workflow
  • Prepare your data for RAG
  • Find relevant data with vector search
  • Rerank your retrieved results
  • Exercise - Set up RAG
  • Module assessment
• Implement multi-stage reasoning in Azure Databricks
  • What are multi-stage reasoning systems?
  • Explore LangChain
  • Explore LlamaIndex
  • Explore Haystack
  • Explore the DSPy framework
  • Exercise - Implement multi-stage reasoning with LangChain
  • Module assessment
• Fine-tune language models with Azure Databricks
  • What is fine-tuning?
  • Prepare your data for fine-tuning
  • Fine-tune an Azure OpenAI model
  • Exercise - Fine-tune an Azure OpenAI model
  • Module assessment
• Evaluate language models with Azure Databricks
  • Compare LLM and traditional ML evaluations
  • Evaluate LLMs and AI systems
  • Evaluate LLMs with standard metrics
  • Describe LLM-as-a-judge for evaluation
  • Exercise - Evaluate an Azure OpenAI model
  • Module assessment
• Review responsible AI principles for language models in Azure Databricks
  • What is responsible AI?
  • Identify risks
  • Mitigate issues
  • Use key security tooling to protect your AI systems
  • Exercise - Implement responsible AI
  • Module assessment
• Implement LLMOps in Azure Databricks
  • Transition from traditional MLOps to LLMOps
  • Understand model deployments
  • Describe MLflow deployment capabilities
  • Use Unity Catalog to manage models
  • Exercise - Implement LLMOps
  • Module assessment