• Data Engineers and Data Scientists
• Machine Learning Engineers
• AI Developers and Cloud Engineers
• Professionals working with large-scale data and AI solutions on Databricks
• Individuals preparing for the Databricks Certified Generative AI Engineer Associate certification

• Basic understanding of Databricks platform and its functionalities
• Knowledge of Python programming and machine learning concepts
• Familiarity with distributed computing frameworks like Apache Spark

Course Outline:

Module 1: Introduction to Generative AI and Databricks Lakehouse

• Overview of Generative AI and its applications
• Databricks Lakehouse architecture for AI workflows
• Integrating data engineering and AI pipelines
• Overview of Databricks MLflow for model lifecycle management Hands-On:
• Setting up the Databricks environment for AI projects
• Exploring Databricks Lakehouse for data preparation Module 2: Working with Large Language Models (LLMs)
• Introduction to LLMs (GPT, BERT, ) and their architectures
• Pre-training fine-tuning of LLMs
• Tokenization and embeddings for text data
• Distributed training and inference for LLMs in Databricks Hands-On:
• Fine-tuning GPT on domain-specific datasets using Databricks notebooks
• Implementing distributed training for text generation using Apache Spark Module 3: Generative Image Models and Applications
• Overview of GANs, VAEs, and diffusion models
• Applications of generative image models (e.g., art generation, image restoration)
• Training image generation models on Databricks clusters Hands-On:
• Training a Stable Diffusion model for custom image generation
• Implementing distributed training for generative image models Module 4: MLOps for Generative AI
• Understanding MLOps and its importance in Generative AI projects
• Model registry, versioning, and deployment with MLflow
• Automating data pipelines for training and inference
• Monitoring and managing AI models in production Hands-On:
• Building an end-to-end MLOps pipeline for a Generative AI project
• Deploying a generative model using MLflow on Databricks Module 5: Scalability, Optimization, and Governance
• Scaling Generative AI workflows on Databricks clusters
• Optimizing resource allocation for distributed AI workloads
• Ethical considerations and AI governance on Databricks
• Handling data privacy and security in Generative AI applications Hands-On:
• Optimizing distributed inference for LLMs using Databricks
• Implementing data governance policies for AI projects

Module 6: Capstone Project

Participants will apply all the skills learned in the course to design and implement a full-scale Generative AI project using Databricks.

Project Examples:

• Building a domain-specific chatbot powered by LLMs and Databricks
• Training and deploying a custom image generation model on Databricks
• Developing a real-time text-to-image AI system using Databricks pipelines

Training Material Provided

• Comprehensive course slides and notes
• Pre-configured Databricks notebooks for hands-on activities
• Sample datasets and pre-trained models
• Certification preparation guide

Any Additional Information

Any additional information about Labs / Software Installs required for the course