Gen AI Engineering with Databricks

• Familiarity with natural language processing concepts
• Familiarity with prompt engineering/prompt engineering best practices
• Familiarity with the Databricks Data Intelligence Platform
• Familiarity with RAG (preparing data, building a RAG architecture, concepts like embedding, vectors, vector databases, etc.)
• Experience with building LLM applications using multi-stage reasoning LLM chains and agents
• Familiarity with Databricks Data Intelligence Platform tools for evaluation and governance.

• Generative AI Solution Development: This is your introduction to contextual generative AI solutions using the retrieval-augmented generation (RAG) method. First, you’ll be introduced to RAG architecture and the significance of contextual information using Mosaic AI Playground. Next, we’ll show you how to prepare data for generative AI solutions and connect this process with building a RAG architecture. Finally, you’ll explore concepts related to context embedding, vectors, vector databases, and the utilization of Mosaic AI Vector Search.
• Generative AI Application Development: Ready for information and practical experience in building advanced LLM applications using multi-stage reasoning LLM chains and agents? In this module, you'll first learn how to decompose a problem into its components and select the most suitable model for each step to enhance business use cases. Following this, we’ll show you how to construct a multi-stage reasoning chain utilizing LangChain and HuggingFace transformers. Finally, you’ll be introduced to agents and will design an autonomous agent using generative models on Databricks.
• Generative AI Application Evaluation and Governance: This is your introduction to evaluating and governing generative AI systems. First, you’ll explore the meaning behind and motivation for building evaluation and governance/security systems. Next, we’ll connect evaluation and governance systems to the Databricks Data Intelligence Platform. Third, we’ll teach you about a variety of evaluation techniques for specific components and types of applications. Finally, the course will conclude with an analysis of evaluating entire AI systems with respect to performance and cost.
• Generative AI Application Deployment and Monitoring: Ready to learn how to deploy, operationalize, and monitor generative deploying, operationalizing, and monitoring generative AI applications? This module will help you gain skills in the deployment of generative AI applications using tools like Model Serving. We’ll also cover how to operationalize generative AI applications following best practices and recommended architectures. Finally, we’ll discuss the idea of monitoring generative AI applications and their components using Lakehouse Monitoring.

Module 1: Generative AI Solution Development

• Introduction to RAG
• Preparing Data for RAG Solutions
• Vector Search
• Assembling and Evaluating a RAG Application

Module 2: Generative AI Application Development

• Foundations of Compound AI Systems
• Building Multi-Stage Reasoning Chains
• Agents and Cognitive Architectures

Module 3: Generative AI Application Evaluation and Governance

• Importance of Evaluating GenAI Applications
• Securing and Governing GenAI Applications
• GenAI Evaluation Techniques
• End-to-end Application Evaluation

Module 4: Generative AI Application Deployment and Monitoring

• Model Deployment Fundamentals
• Batch Deployment
• Real-Time Deployment
• AI System Monitoring
• LLMOps Concepts

QA reserves the right to improve the specification and format of its courses for the benefit of its customers without notice to the customer.