Master generative AI engineering using Azure Databricks, covering model deployment, RAG, and MLOps practices.
Overview
This learning path guides learners through implementing advanced generative AI engineering solutions on Azure Databricks. It covers foundational concepts of large language models, practical prompt engineering, and techniques for fine-tuning models. Learners will explore retrieval augmented generation (RAG) architectures, use vector databases, and implement MLOps best practices to deploy and manage generative AI applications effectively. Ideal for AI engineers and data scientists looking to leverage Databricks for scalable AI solutions.
Learning Outcomes
Understand core generative AI concepts and large language models (LLMs).
Implement Retrieval Augmented Generation (RAG) patterns using Azure Databricks.
Perform fine-tuning of large language models for specific applications.
Deploy and operationalize LLMs using MLflow on Azure Databricks.
Apply MLOps best practices to manage the lifecycle of generative AI solutions.
Utilize vector databases to enhance data retrieval for RAG architectures.
Master prompt engineering techniques to optimize LLM responses.