Catalyze AI:From Python to GenAI & Agentic AI with FastAPI
Program Details
Hands-on, Project-Driven
9 Months
Online/offline
Beginer/Intermediate
Catalyze AI is a comprehensive, project-driven program that takes you from Python fundamentals to building scalable AI-powered backend systems and multi-agent applications. You will learn how modern AI products are designed end-to-end — from APIs and databases to RAG pipelines and agentic workflows using tools like FastAPI, LangChain, LangGraph, and CrewAI.
This program is designed for developers who want to go beyond notebooks and build production-ready AI applications
Skills Covered:
-
Python Programming
-
Backend Development
-
Frontend Development
-
Database Design
-
API Integration
-
Large Language Models
-
Retrieval Augmented Generation
-
Prompt Engineering
-
Agentic AI Systems
-
Vector Databases
-
Multi-Agent Architectures
-
Git & Version Control
Career Outcome:
GENAI ENGINEER
Design, build, and deploy advanced AI models—primarily Large Language Models (LLMs) and Generative Adversarial Networks (GANs) transforming research findings into scalable, production-ready applications.
AI RESEARCH ENGINEER
They are responsible for advancing state-of-the-art AI systems, conducting experiments, and transforming research findings into scalable, production-ready applications.
AI BACKEND DEVELOPER
Designing API backends, preprocessing data for AI models, building server-side logic, and integrating AI models into applications
Python-React Full Stack Developer
Designing, developing, and maintaining both the front-end (user interface) with React.js and the back-end (server-side logic and databases) with Python.
Backend Developer - Python
Builds and maintains the server-side logic, databases, and API integrations that power web application functionality. They write clean, secure, and scalable Python code using frameworks like Django or FastAPI to connect user-facing frontend elements to data storage solutions.
Frontend Developer - React
Developing reusable components, managing application state, integrating APIs, optimizing performance for speed, and collaborating with designers/backend teams to translate UI/UX designs into high-quality code.
Backend Engineer
Builds and maintains the server-side logic, databases, and APIs that power web applications, ensuring high performance, scalability, and security
Program Outline:
Curriculam Road map
- Understanding AI product architecture
- Designing scalable backend systems
- API-first development approach
- Database schema design fundamentals
- System design for AI-powered applications
- Introduction to Agile & structured development workflow
- How the web works (HTTP, REST, APIs)
- Client-server architecture
- JSON & data exchange formats
- Introduction to API testing tools
- Understanding authentication flows
- Introduction to React fundamentals
- Components, props, and state
- Calling backend APIs
- Handling forms and authentication
- Connecting React UI to FastAPI backend
- Building simple AI-powered dashboards
- Python Basics & Syntax
- Control Flow & Loops
- Functions & Modular Code
- Python Collections & Comprehensions
- Advanced Python Concepts
- Async programming fundamentals
- Database integration basics
- Database fundamentals
- Working with PostgreSQL
- SQL queries & joins
- Database schema design
- ORM with SQLAlchemy
- Async database operations
- Managing migrations
- Introduction to FastAPI
- REST API development
- Pydantic models & validation
- Authentication & Authorization (JWT-based)
- Error handling & middleware
- Async API development
- Production-ready backend structure
- Introduction to Generative AI
- Large Language Model landscape (OpenAI, Gemini, open-source models)
- How LLMs work (tokens, embeddings, transformers)
- Prompt Engineering techniques
- API-based LLM integration
- Using embeddings for semantic search
Foundations of Agentic AI
- What are AI agents?
- Tool-using agents
- Multi-agent system architecture
Embeddings & Vector Databases
- Vector search fundamentals
- Working with ChromaDB
- Working with Qdrant
- Semantic search pipelines
Retrieval Augmented Generation (RAG)
- Building RAG systems
- Chunking strategies
- Hybrid retrieval
- Memory & context management
LangChain & LangGraph
- Building tool-using AI with LangChain
- Designing stateful workflows with LangGraph
- Agent memory & multi-step reasoning
CrewAI & Collaborative Agents
- Building collaborative AI systems using CrewAI
- Role-based agents
- Task delegation & coordination
AI Application Layer
- Building UI with Streamlit
- Deploying multi-agent applications
- Logging, monitoring & evaluation
You will build a production-ready multi-agent AI system that includes:
- FastAPI backend
- PostgreSQL database
- RAG pipeline
- Vector database integration
- Multi-agent orchestration
- AI-powered API endpoints
- Frontend dashboard
Tools Covered:
TECH STACK YOU’LL MASTER
-
PROGRAMMING LANGUAGE:
Python
-
AI FRAMEWORKS & CONCEPTS
LANGCHAIN
LANGGRAPH
RAG (RETRIEVAL-AUGMENTED GENERATION) -
DATABASES
POSTGRESQL
VECTOR DBS (CHROMADB, QDRANT) -
TOOLS & PROJECT MANAGEMENT
GIT
TRELLO
SCRUM (AGILE METHODOLOGY – SPRINTS, STAND-UPS, SPRINT PLANNING, RETROSPECTIVES)
Ready to Accelerate Your Career
Join the next cohort of Catalyze AI and transform from a coder to an AI Architect.