Learn to build, deploy, and manage generative AI applications using LLMs and the Databricks Lakehouse Platform.
Overview
This comprehensive course delves into the rapidly evolving field of generative AI, focusing on practical engineering techniques to leverage large language models (LLMs) effectively. Participants will gain hands-on experience with the Databricks Lakehouse Platform, learning to build, deploy, and manage production-ready generative AI applications. Key topics include prompt engineering, fine-tuning LLMs, Retrieval Augmented Generation (RAG) architectures, model evaluation, and MLOps best practices for GenAI. It's designed for developers, data scientists, and machine learning engineers eager to integrate cutting-edge AI into their workflows.
Learning Outcomes
Understand the fundamentals of generative AI and LLMs, including their architectures and capabilities.
Master prompt engineering techniques to optimize LLM performance and output.
Implement Retrieval Augmented Generation (RAG) applications for context-aware responses.
Learn to fine-tune LLMs using various techniques on the Databricks Lakehouse Platform.
Develop strategies for deploying, monitoring, and managing generative AI applications in production.
Evaluate and benchmark the performance of generative AI models.
Apply MLOps best practices for robust GenAI lifecycle management.