Generative Artificial Intelligence (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.
- Level: Intermediate
- Product: Azure
- Role: AI Engineer, Data Scientist
- Subject: Artificial intelligence, Machine learning
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.
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.
Skills Covered
- Get started with language models in Azure Databricks’
- Implement Retrieval Augmented Generation (RAG) with Azure Databricks
- Implement multi-stage reasoning in Azure Databricks
- Fine-tune language models with Azure Databricks
- Evaluate language models with Azure Databricks
- Review responsible AI principles for language models in Azure Databricks
- Implement LLMOps in Azure Databricks
Prerequisites
Before starting this module, you should be familiar with fundamental AI concepts and Azure Databricks. Consider completing the Get started with artificial intelligence learning path and the Explore Azure Databricks module first.
Target Audience
his 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.
Module 1: Get started with language models in Azure Databricks
Large Language Models (LLMs) have revolutionized various industries by enabling advanced natural language processing (NLP) capabilities. These language models are utilized in a wide array of applications, including text summarization, sentiment analysis, language translation, zero-shot classification, and few-shot learning.
Learning objectives
In this module, you learn how to:
- Describe Generative AI.
- Describe Large Language Models (LLMs).
- Identify key components of LLM applications.
- Use LLMs for Natural Language Processing (NLP) tasks.
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.
Learning objectives
In this module, you learn how to:
- Set up a RAG workflow.
- Prepare your data for RAG.
- Retrieve relevant documents with vector search.
- Improve model accuracy by reranking your search results.
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.
Learning objectives
In this module, you learn how to:
- Identify the need for multi-stage reasoning systems.
- Describe a multi-stage reasoning workflow.
- Implement multi-stage reasoning with libraries like LangChain, LlamaIndex, Haystack, and the DSPy framework.
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.
Learning objectives
In this module, you learn how to:
- Understand when to use fine-tuning.
- Prepare your data for fine-tuning.
- Fine-tune an Azure OpenAI model.
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.
Learning objectives
In this module, you learn how to:
- Evaluate LLM evaluation models
- Describe the relationship between LLM evaluation and AI system evaluation
- Describe standard LLM evaluation metrics like accuracy, perplexity, and toxicity
- Describe LLM-as-a-judge for evaluation
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.
Learning objectives
In this module, you learn how to:
- Describe the responsible AI principles for implementation of language models.
- Identify the ethical considerations for language models.
- Mitigate the risks associated with language models.
- Implement key security tooling for language models.
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.
Learning objectives
In this module, you learn how to:
- Describe the LLM lifecycle overview.
- Identify the model deployment option that best fits your needs.
- Use MLflow and Unity Catalog to implement LLMops.
Dates & Locations
August 20, 2026 - August 20, 2026
August 20, 2026 - August 20, 2026
November 19, 2026 - November 19, 2026
November 19, 2026 - November 19, 2026
Exam & Certification
Note: There is no exam directly associated with this course. However, Microsoft offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2025 and beyond. Achieving Microsoft certification is one of the most effective ways to validate your skills and accelerate your career.
With our expert-led training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
- Prove your expertise with a globally respected credential recognized by employers worldwide.
- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
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