Implement Generative AI engineering with Azure Databricks

After completing this course, students will be able 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.
• 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.
• 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.
• Describe the LLM lifecycle overview.
• Identify the model deployment option that best fits your needs.
• Use MLflow and Unity Catalog to implement LLMops.

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.
 

Before starting this module, you should be familiar with fundamental Azure Databricks concepts. Consider completing the Get started with artificial intelligence learning path and the Explore Azure Databricks module first.
 

Get started with language models in Azure Databricks

• 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

Implement Retrieval Augmented Generation (RAG) with Azure Databricks

• 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

Implement multi-stage reasoning in Azure Databricks

• 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

Fine-tune language models with Azure Databricks

• Introduction
• What is fine-tuning?
• Prepare your data for fine-tuning
• Fine-tune an Azure OpenAI model
• Exercise - Fine-tune an Azure OpenAI model
• Module assessment

Evaluate language models with Azure Databricks

• 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

Review responsible AI principles for language models in Azure Databricks

• Introduction
• What is responsible AI?
• Identify risks
• Mitigate issues
• Use key security tooling to protect your AI systems
• Exercise - Implement responsible AI
• Module assessment

Implement LLMOps in 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