DP-3028: Implement Generative AI engineering with Azure Databricks

Important: This course will be available on 07/18/25

Course DP-3028: Implement Generative AI engineering with Azure Databricks

This course covers generative AI engineering in Azure Databricks, using Spark to explore, refine, evaluate, and integrate advanced language models. It teaches how to implement techniques such as 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 responsible AI practices for deploying AI solutions and how to manage models in production using LLMOps (Large Language Model Operations) in Azure Databricks.

Level: Intermediate - Role: AI Engineer, Data Scientist - Product: Azure - Subject: Artificial Intelligence, Machine Learning

Course aimed at

This course is designed for data scientists, machine learning engineers, and other AI professionals who want to build generative AI applications with Azure Databricks. It is intended for professionals familiar with fundamental AI concepts and the Azure Databricks platform.

Objectives of the official course DP-3028

• Introduction to Language Models (LLMs): Understand the fundamentals of generative AI, language models, and their application in natural language processing (NLP) tasks.
• Implement RAG (Retrieval-Augmented Generation): Learn to prepare data, perform vector searches, and apply re-ranking techniques to improve the accuracy of the generated responses.
• Develop multi-stage reasoning: Use frameworks such as LangChain, LlamaIndex, Haystack, and DSPy to build complex reasoning flows.
• Fine-tuning language models: Prepare data and fine-tune Azure OpenAI models for specific tasks.
• Evaluating language models: Compare traditional assessments with LLM-specific metrics, including the "LLM-as-a-judge" approach.
• Apply responsible AI principles: Identify risks, mitigate issues, and apply security tools to protect AI systems.
• Implement LLMOps: Transition from MLOps to LLMOps, manage deployments with MLflow, and use Unity Catalog for version control and model security.

Contents of the official Azure Databricks DP-3028 course

Module 1 Introduction to Language Models in Azure Databricks

• Introduction
  
• Description of generative artificial intelligence
  
• Understanding Major Language Models (LLM)
  
• Identifying the key components of LLM applications
  
• Using LLM for Natural Language Processing (NLP) Tasks
  
• Exercise: Exploring Language Models

Module 2: Implementing Recovery Augmented Generation (RAG) with Azure Databricks

• Introduction
  
• Exploring the main concepts of a RAG workflow
  
• Preparing data for RAG
  
• Searching for relevant data with the search vector
  
• Reassignment of recovered results
  
• Exercise: RAG Configuration

Module 3: Implementing Multi-Stage Reasoning in Azure Databricks

• Introduction
  
• What are multi-stage reasoning systems?
  
• Explore LangChain
  
• Exploring LlamaIndex
  
• Explore Haystack
  
• Explore the DSPy framework
  
• Exercise: Implementing multi-phase reasoning with LangChain

Module 4: Tuning Language Models with Azure Databricks

• Introduction
  
• What is adjustment?
  
• Preparing data for fitting
  
• Tuning an Azure OpenAI Model
  
• Exercise: Tuning an Azure OpenAI Model

Module 5: Evaluating Language Models with Azure Databricks

• Introduction
  
• Comparison of LLM and traditional ML assessments
  
• Evaluation of virtual machines and artificial intelligence systems
  
• LLM Assessment with Standard Metrics
  
• LLM-as-a-judge description for assessment
  
• Exercise: Evaluating an Azure OpenAI Model

Module 6: Reviewing Responsible AI Principles for Language Models in Azure Databricks

• Introduction
  
• What is responsible artificial intelligence?
  
• Identify risks
  
• Mitigation of problems
  
• Using key security tools to protect artificial intelligence systems
  
• Exercise: Implementing Responsible AI

Module 7: Implementing LLMOps on Azure Databricks

• Introduction
  
• Transition from traditional MLOps to LLMOps
  
• Understanding model implementations
  
• Description of MLflow implementation features
  
• Using Unity Catalog to manage models
  
• Exercise: Implement LLMOps

Prerequisites

Before starting this module, you should be familiar with fundamental concepts of artificial intelligence and Azure Databricks.

Language

• Course: English / Spanish