Cloud Private

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

1 day
AzureAIAzure DevOps ServicesPythonSpark

Learn practical generative AI engineering with Azure Databricks, including RAG, multi-stage reasoning, LLM fine-tuning, evaluation, responsible AI, and LLMOps.

Talk to a Learning ConsultantRequest a Private Class

Register or Request Training

  • Private class for your team
  • Live expert instructor
  • Online or on‑location
  • Customizable agenda
  • Proposal responses same day as request
Request Proposal

Course Overview

This course covers generative AI engineering on Azure Databricks using Spark to explore, fine-tune, evaluate, and integrate advanced language models. You will learn to implement retrieval-augmented generation (RAG) and multi-stage reasoning, fine-tune language models for specific tasks, and evaluate model performance. The course also addresses responsible AI practices and LLMOps for managing and deploying models in production.

Course Benefits

  • Understand generative AI and large language model concepts
  • Identify key LLM application components and use LLMs for natural language processing tasks
  • Implement retrieval-augmented generation using data preparation, vector search, and reranking
  • Explore multi-stage reasoning with LangChain, LlamaIndex, Haystack, and DSPy
  • Prepare data for fine-tuning and fine-tune an Azure OpenAI model
  • Evaluate LLMs and AI systems using standard metrics and LLM-as-a-judge techniques
  • Identify and mitigate AI risks using responsible AI principles and security tooling
  • Apply LLMOps practices using MLflow deployment capabilities and Unity Catalog

Delivery Methods

Private Class
Delivered for your team at your site or online.

Microsoft Certified Partner

Webucator is a Microsoft Certified Partner. This class uses official Microsoft courseware and will be delivered by a Microsoft Certified Trainer (MCT).

Microsoft Certified Partner

Course Outline

  1. Get started with language models in Azure Databricks
    1. Understand generative AI
    2. Understand large language models (LLMs)
    3. Identify key components of LLM applications
    4. Use LLMs for natural language processing (NLP) tasks
  2. Implement retrieval-augmented generation (RAG) with Azure Databricks
    1. Explore the main concepts of a RAG workflow
    2. Prepare your data for RAG
    3. Find relevant data with vector search
    4. Rerank your retrieved results
  3. Implement multi-stage reasoning in Azure Databricks
    1. Understand multi-stage reasoning systems
    2. Explore LangChain
    3. Explore LlamaIndex
    4. Explore Haystack
    5. Explore the DSPy framework
  4. Fine-tune language models with Azure Databricks
    1. Understand fine-tuning
    2. Prepare your data for fine-tuning
    3. Fine-tune an Azure OpenAI model
  5. Evaluate language models with Azure Databricks
    1. Explore LLM evaluation
    2. Evaluate LLMs and AI systems
    3. Evaluate LLMs with standard metrics
    4. Describe LLM-as-a-judge for evaluation
  6. Review responsible AI principles for language models in Azure Databricks
    1. Understand responsible AI
    2. Identify risks
    3. Mitigate issues
    4. Use key security tooling to protect AI systems
  7. Implement LLMOps in Azure Databricks
    1. Transition from traditional MLOps to LLMOps
    2. Understand model deployments
    3. Describe MLflow deployment capabilities
    4. Use Unity Catalog to manage models

Class Materials

Each student receives a comprehensive set of materials, including course notes and all class examples.

Class Prerequisites

Experience in the following is required for this Azure class:

Familiarity with fundamental Azure Databricks concepts.

Experience in the following would be useful for this Azure class:

Familiarity with fundamental AI and Azure Databricks concepts is recommended.

Have questions about this course?

We can help with curriculum details, delivery options, pricing, or anything else. Reach out and we’ll point you in the right direction.

Talk to a Learning Consultant