Implement Generative AI Engineering with Azure Databricks (DP-3028)

This course covers generative AI engineering on Azure Databricks, using Spark to explore, fine-tune, evaluate, and integrate advanced language models. It teaches how to implement techniques like retrieval-augmented generation (RAG) and multi-stage reasoning, as well as how to fine-tune large language models for specific tasks and evaluate their performance. Students will also learn about responsible AI practices for deploying AI solutions and how to manage models in production using LLMOps (Large Language Model Operations) on Azure Databricks.

Implement Generative AI Engineering with Azure Databricks (DP-3028) Benefits

• In this course, you will:

  • Gain hands-on experience implementing Retrieval-Augmented Generation (RAG) and fine-tuning large language models (LLMs).
  • Explore multi-stage reasoning techniques using LangChain, LlamaIndex, Haystack, and DSPy.
  • Understand and apply LLMOps practices for model deployment, monitoring, and governance with MLflow and Unity Catalog.
  • Incorporate responsible AI principles, including risk mitigation and ethical considerations.
  • Build and operationalize generative AI solutions using Azure Databricks and Apache Spark.
  • Acquire in-demand generative AI skills in a focused, one-day training format.

• Prerequisites

  Before starting this module, you should be familiar with fundamental Azure Databricks concepts.

Azure Databricks Generative AI Course Outline

Learning Objectives

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

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

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

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

Evaluate language models with Azure Databricks

• Explore LLM evaluation
• Evaluate LLMs and AI systems
• Evaluate LLMs with standard metrics
• Describe LLM-as-a-judge for evaluation
• Exercise – Evaluate an Azure OpenAI model

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

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