Build generative AI applications and deploy LLM-powered solutions at scale using Databricks
This course covers key generative AI concepts including retrieval-augmented generation (RAG), prompt engineering, model evaluation, and deployment using tools such as MLflow, Mosaic AI, vector search and model serving within Databricks.
- Why get trained: Learn how to design and deploy generative AI applications using RAG, MLflow, Mosaic AI, vector search and model serving on the Databricks platform.
- Why it matters: Generative AI capabilities enable organizations to build intelligent applications, automate content generation and scale AI-driven solutions across business functions.
- Who should attend: Data scientists, machine learning engineers and AI practitioners responsible for developing and deploying generative AI and LLM-based solutions.
Build the capability to design, deploy and manage generative AI solutions using Databricks with Trainocate. HRD Corp Claimable.
Overview
This course is aimed at data scientists, machine learning engineers, and other data practitioners who want to build generative AI applications using the latest and most popular frameworks and Databricks capabilities.
Below, we describe each of the four, four-hour modules included in this course.
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.
Skills Covered
- Generative AI Solution Development
- Generative AI Application Development
- Generative AI Application Evaluation and Governance
- Generative AI Application Deployment and Monitoring
Prerequisites
- Generative AI Fundamentals
- Get Started with Databricks for Generative AI course
- Understanding of natural language processing concepts
- Familiarity with prompt engineering/prompt engineering best practices
- Experience with the Databricks Data Intelligence Platform
- Experience with RAG Â (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
Target Audience
- Everyone who is interested
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
Dates & Locations
July 28, 2026 - July 31, 2026
October 13, 2026 - October 14, 2026
Exam & Certification
Databricks Certified Generative AI Engineer Associate.
The Databricks Certified Generative AI Engineer Associate certification exam assesses an individual’s ability to design and implement LLM-enabled solutions using Databricks. This includes problem decomposition to break down complex requirements into manageable tasks as well as choosing appropriate models, tools and approaches from the current generative AI landscape for developing comprehensive solutions.
It also assesses Databricks-specific tools such as Vector Search for semantic similarity searches, Model Serving for deploying models and solutions, MLflow for managing a solution lifecycle, and Unity Catalog for data governance. Individuals who pass this exam can be expected to build and deploy performant RAG applications and LLM chains that take full advantage of Databricks and its toolset.
The exam covers:
- Design Applications – 14%
- Data Preparation – 14%
- Application Development – 30%
- Assembling and Deploying Apps – 22%
- Governance – 8%
- Evaluation and Monitoring – 12%
Training & Certification Guide
Frequently Asked Questions
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