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

Course Information

Course Name

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

Duration

1 Day

Overview

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. Learners will also explore responsible AI practices for deploying AI solutions and how to manage models in production using LLMOps (Large Language Model Operations) on Azure Databricks.

Audience Profile

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

Prerequisities

You should be familiar with data analytics and engineering concepts and terminology.

At Course Completion

Course Outline

Module 1: Get started with language models in Azure Databricks

Implement Generative AI engineering with Azure Databricks

Generative AI engineering with Azure Databricks uses the platform’s capabilities to explore, fine-tune, evaluate, and integrate advanced language models. By using Apache Spark’s scalability and Azure Databricks’ collaborative environment, you can design complex AI systems.

Module 1: Get started with language models in Azure Databricks

Lessons

• Introduction

• 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

Module 2: Implement Retrieval Augmented Generation (RAG) with Azure Databricks

Retrieval Augmented Generation (RAG) is an advanced technique in natural language processing that enhances the capabilities of generative models by integrating external information retrieval mechanisms. When you use both generative models and retrieval systems, RAG dynamically fetches relevant information from external data sources to augment the generation process, leading to more accurate and contextually relevant outputs.

• Introduction

• 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

Module 3: Implement multi-stage reasoning in Azure Databricks

Multi-stage reasoning systems break down complex problems into multiple stages or steps, with each stage focusing on a specific reasoning task. The output of one stage serves as the input for the next, allowing for a more structured and systematic approach to problem-solving.

• Introduction

• 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

Module 4: Fine-tune language models with Azure Databricks

Fine-tuning uses Large Language Models’ (LLMs) general knowledge to improve performance on specific tasks, allowing organizations to create specialized models that are more accurate and relevant while saving resources and time compared to training from scratch.

• Introduction

• What is fine-tuning?

• Prepare your data for fine-tuning

• Fine-tune an Azure OpenAI model

• Module assessment

 Module 5: Evaluate language models with Azure Databricks

In this module, you explore Large Language Model evaluation using various metrics and approaches, learn about evaluation challenges and best practices, and discover automated evaluation techniques including LLM-as-a-judge methods.

• Introduction

• 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

• Module assessment

Module 6: Review responsible AI principles for language models in Azure Databricks

When working with Large Language Models (LLMs) in Azure Databricks, it’s important to understand the responsible AI principles for implementation, ethical considerations, and how to mitigate risks. Based on identified risks, learn how to implement key security tooling for language models.

• Introduction

• What is responsible AI?

• Identify risks

• Mitigate issues

• Use key security tooling to protect your AI systems

• Exercise – Implement responsible AI

• Module assessment

Module 7: Implement LLMOps in Azure Databricks

Streamline the implementation of Large Language Models (LLMs) with LLMOps (LLM Operations) in Azure Databricks. Learn how to deploy and manage LLMs throughout their lifecycle using Azure Databricks.

• Introduction

• Transition from traditional MLOps to LLMOps

• Understand model deployments

• Describe MLflow deployment capabilities

• Use Unity Catalog to manage models

• Exercise – Implement LLMOps

Module assessment

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