This course contains the use of artificial intelligence.Welcome to this Databricks Generative AI Engineer certification preparation course!We'll cover how to break down complex AI problems, select the right tools from the GenAI ecosystem, and master Databricks-specific technologies: Vector Search for semantic retrieval, Model Serving for real-time deployment, MLflow for managing the model lifecycle, and Unity Catalog for data governance — ultimately enabling you to build and deploy RAG applications and LLM chains in production.For this course, I chose to work with the Databricks Free Edition, a free version accessible to everyone. While it doesn't include features still in beta, I keep it regularly updated. The course blends theoretical presentations with hands-on workshops illustrating key concepts, all grouped together in a GitHub project.This course is aimed at developers — whether software engineers, data scientists, or data engineers — who have already built POCs using frameworks such as LlamaIndex, LangChain, or CrewAI. If you have a working knowledge of Python and SQL and want to master every step of taking your work to production on Databricks — then validate those skills through certification — this course is for you.If this is what you're looking for, I invite you to join us. I hope you' ll enjoy exploring all these concepts as much as I did — happy learning!
What you'll learn
understand how to tackle complex AI problems
select appropriate tools from the Generative AI ecosystem
utilize Databricks technologies for real-time deployment
manage the model lifecycle with MLflow
implement data governance using Unity Catalog
build and deploy applications in production
Course objectives
prepare for the Databricks Generative AI Engineer certification
develop practical skills through hands-on workshops