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.

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

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.

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.

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.

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.