Databricks Generative AI Engineer Associate Certification Bootcamp
Published by O'Reilly Media, Inc.
Course outcomes
- Prepare for the exam with practice questions and hands-on exercises
- Explore real world scenarios of LLM-enabled solutions in Databricks
Course description
Join expert Yasir Khan to learn the fundamentals of designing and implementing LLM-enabled solutions using Databricks, in preparation for the Databricks Certified Generative AI Engineer Associate certification exam. You’ll understand how to break down complex problems, select appropriate models and tools, and leverage Databricks-specific features like Vector Search, Model Serving, MLflow, and Unity Catalog. At the end of this two-day course, you’ll be equipped to build and deploy high-performance retrieval-augmented generation (RAG) applications and LLM chains.
NOTE: With today’s registration, you’ll be signed up for both sessions. Although you can attend either of the sessions individually, we recommend participating in both.
What you’ll learn and how you can apply it
- Learn to use Databricks-specific tools like Vector Search, Model Serving, MLflow, and Unity Catalog to manage, deploy, and optimize generative AI solutions
- Develop and deploy RAG applications and LLM pipelines, enhancing AI-driven insights and decision-making with efficient data retrieval and governance
- Apply your knowledge through quizzes and hands-on labs that mirror the Databricks Generative AI Engineer Associate certification exam format, ensuring you’re ready for the test
This live event is for you because...
- You want to ensure you’re fully prepared for the Databricks Generative AI Engineer Associate exam.
- You value practical, hands-on experience and want to apply your learning directly in real-world scenarios.
- You’re an IT professional looking to deepen your Databricks generative AI expertise and advance your career with a recognized certification.
Prerequisites
- A foundational understanding of generative AI concepts such as vector search, LLM, RAG, and best practices
- Proficiency in programming languages such as Python
Recommended preparation:
- Bookmark the course Confluence page (instructions for each lab setup and execution in your Databricks environment will be given in the course)
Recommended follow-up:
- Read and follow “Databricks Gen AI Engineer Associate Certification – Exam Guide” (documentation)
Schedule
The time frames are only estimates and may vary according to how the class is progressing.
Day 1: Foundations of Databricks Generative AI Applications and Data Preparation
Introduction to Databricks generative AI (10 minutes)
- Presentation: Setting up Databricks on Azure
- Q&A
Designing generative applications (50 minutes)
- Presentation and hands-on exercise: Designing prompts for AI applications; selecting model tasks and chain components
- Q&A
- Break
Data preparation for retrieval-augmented generation (60 minutes)
- Presentation and hands-on exercise: Document chunking strategies and model constraints; data extraction and preparation; evaluating retrieval performance
- Q&A
- Break
Data retrieval tools and prompt engineering (60 minutes)
- Presentation and hands-on exercise: Data extraction; LangChain tools; prompt formats; quality assessment; prompt augmentation
- Q&A
- Break
Model selection, guardrails, and optimization (60 minutes)
- Presentation and hands-on exercise: LLM guardrails; metaprompts; agent prompt templates; model selection
- Q&A
Day 2: Deploying Applications, Governance, Evaluation and Monitoring
Building and coding AI application chains (55 minutes)
- Presentation and hands-on exercise: Coding a chain; preprocessing and post-processing models; control access from model serving endpoints
- Q&A
- Break
Deploying and managing AI applications (55 minutes)
- Presentation and hands-on exercise: Vector Search index; RAG applications; model registration using Unity Catalog and MLflow
- Q&A
- Break
Governance (55 minutes)
- Presentation and hands-on exercise: Masking and guardrail techniques; text mitigation; legal/licensing requirements for data sources
- Q&A
- Break
Evaluation and monitoring (55 minutes)
- Presentation and hands-on exercise: LLM architecture; evaluation metrics; inference logging; controlling LLM costs
- Q&A
- Break
Wrap-up and Q&A (20 minutes)
Your Instructor
Yasir Khan
Dr. Yasir Khan is the founder of 38 Labs, an Enterprise Data & AI consulting group with offices based out of Paris, New York and Bangalore. He holds a PhD in AI and is an instructor at O’Reilly Media mentoring future experts on AI transformation, machine learning, enterprise solutions and digital transformation. Over his career he has published several articles for leading publishing houses in the field of AI. He speaks at several international conferences such as PyCon, PyData, IEEE. In his spare time he likes flying aircrafts, climbing mountains and traveling.