DP-3028: Implement Generative AI engineering with Azure Databricks Duration: 1 Day

Course details

Module 1: Get started with language models in Azure Databricks
• Understand Generative AI
• Understand 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
• Explore the main concepts of a RAG workflow
• Prepare your data for RAG
• Find relevant data with vector search
• Rerank your retrieved results

Module 3: Implement multi-stage reasoning in Azure Databricks
• What are multi-stage reasoning systems?
• Explore LangChain
• Explore LlamaIndex
• Explore Haystack
• Explore the DSPy framework

Module 4: Fine-tune language models with Azure Databricks
• What is fine-tuning?
• Prepare your data for fine-tuning
• Fine-tune an Azure OpenAI model

Module 5: Evaluate language models with Azure Databricks
• Explore LLM evaluation
• Evaluate LLMs and AI systems
• Evaluate LLMs with standard metrics
• Describe LLM-as-a-judge for evaluation

Module 6: Review responsible AI principles for language models in Azure Databricks
• What is responsible AI?
• Identify risks
• Mitigate issues
• Use key security tooling to protect your AI systems

Module 7: Implement LLMOps in Azure Databricks
• Transition from traditional MLOps to LLMOps
• Understand model deployments
• Describe MLflow deployment capabilities
• Use Unity Catalog to manage models