Generative AI
Generative AI
This course teaches you how to build, fine-tune, and deploy Generative AI models using MLflow. You’ll work with text generation (LLMs), image generation (Diffusion models), prompt engineering, and containerized deployment. Through hands-on labs, you’ll master MLflow for experiment tracking, model registry, and performance monitoring.
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About the course
The Generative AI Using MLflow course is a complete, hands-on guide to building, managing, and deploying cutting-edge AI models. You’ll begin by understanding Generative AI concepts, model types, and real-world use cases, before diving into MLflow fundamentals for experiment tracking and model management. Using Hugging Face, you’ll work with powerful text generation models like GPT-2, fine-tuning them on custom datasets and logging results to MLflow.You’ll then explore image generation with Stable Diffusion, track prompts and generated images, and compare experimental results. The course covers essential prompt engineering techniques, domain-specific dataset creation, and structured experiment logging. You’ll learn to package and serve models via REST APIs, deploy them in Docker containers with Streamlit/FastAPI, and optimize access through NGINX.Finally, you’ll master model evaluation by logging key metrics, monitoring inference performance, and tracking system usage in MLflow. By the end, you’ll have the skills to take Generative AI projects from concept to production with full reproducibility and scalability.
Course details
Course Content
Python Basics
- Python Installation & Environment Setup (Anaconda / venv)
- Variables, Data Types (str, int, float, bool, list, tuple, dict, set)
- Basic Operators and Expressions
- Control Flow: if-else, for, while loops
- Functions & Return Values
- Lab: Write a Python script to take a sentence and count words, characters, and vowels.
Working with Libraries
- Installing and Importing Libraries (pip, conda)
- Using Popular Libraries (numpy, pandas, matplotlib)
- Reading Documentation & Examples
- Lab: Install numpy and pandas, create a DataFrame from a list of dictionaries, anddisplay basic statistics.
File Handling & Data Processing
- Reading & Writing Text Files
- Working with CSV and JSON
- Handling Exceptions (try-except)
- Lab: Read a CSV file of text prompts and write the first 10 prompts to a new text file.
Introduction to Generative AI
- What is Generative AI?
- Use Cases of Generative AI
- Understanding Tokenization
- Understanding Vector Databases
- Lab: Using a tokenizer
System Prompts for Generative AI
- Understanding Zero Shot Prompting
- Lab: Interacting with OpenAI using Zero Shot Prompting
- Understanding Few Shot Prompting
- Lab: Interacting with OpenAI using Few Shot Prompting
- Understanding Chain-of-Thought Prompting
- Lab: Interacting with OpenAI using CoT Prompting
- Understanding Persona Based Prompting
- Lab: Interacting with OpenAI using Persona Based Prompting
Combining LLMs with Custom Scripts
- Understanding Custom Scripting for LLMs
- Advantages & Disadvantages of Scripting with LLMs
- Lab: Writing Scripts for LLMs
- Lab: Connecting OpenAI to Custom Scripts
Retrieval-Augmented Generation (RAG)
- Understanding the Need of RAG
- Understanding RAG
- Understanding Qdrant Vector DB
- Understanding Langchain
- Lab: Creating a RAG Pipeline Project
- Lab: Deploy RAG on Kubernetes using a Microservice architecture
Retrieval-Augmented Generation in Enterprises
- Understanding RAG issues in Scalability
- Discussing Possible RAG Solutions in Enterprise
- Lab: Deploying Async RAG Using a Queue Service
LangGraph for Agentic AI
- Introduction to LangGraph
- LangGraph vs N8N Comparsion
- Understandings Nodes & Edges for Agentic AI
- Lab: Creating a Simple Agentic AI
- Lab: Creating a Multi Step Agentic AI
Advanced LangGraph for Agentic AI
- Understanding Agentic AI Memory Issues
- Understanding Checkpointing in Agentic AI
- Using MongoDB for Checkpointing
- Lab: Creating Agentic AI Workflow with MongoDB Checkpointing
Memory for Customer Facing AI
- Understanding Contextual Limitations
- Understanding how Recommendation Systems Work
- Need of Memory in Customer Facing Apps
- Understanding how Neo4J Works
- Lab: Deploying AI Chatbot with Neo4J
Model Context Protocol (MCP) & Use Cases
AI Voice Agents and Enterprise Usage
AI GuardRails & Deployments on AWS
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