Databricks Certified Generative AI Engineer Associate

Description:

The Databricks Certified Generative AI Engineer Associate program is tailored to help participants master the integration of Generative AI techniques within the Databricks environment. It covers the end-to-end process of building, fine-tuning, and deploying large-scale generative models, such as GPT and Stable Diffusion, using Databricks’ Lakehouse architecture.

Participants will gain hands-on experience with distributed training, MLOps workflows, and deploying AI solutions in a production environment. This course emphasizes scalability, optimization, and practical implementation of Generative AI within Databricks, preparing participants to achieve certification and excel in real-world AI engineering tasks.

For Certification based Assistance and Mock quizzes please visit: https://certify360.ai/

Duration: 5 Days

Course Code: BDT408

Learning Objectives:

Upon completing this course, participants will:

1. Understand Generative AI fundamentals and how to apply them using
2. Learn about Databricks Lakehouse architecture and its relevance to AI
3. Build, fine-tune, and deploy large language models (LLMs) and generative image
4. Optimize distributed training for generative models using Apache
5. Develop and manage MLOps pipelines for Generative AI
6. Integrate AI solutions into enterprise-scale systems using Databricks’ collaborative
7. Gain insights into managing ethical concerns, data privacy, and AI model

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

• 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

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