Best FastAPI Training in Mohali, Chandigarh & Gurdaspur: Build High-Performance Python Web Services
Step into modern backend development with the comprehensive FastAPI Training program at Future Finders. Designed for software engineers, web developers, students, and tech professionals, this course builds practical skills for developing production-ready FastAPI applications and high-concurrency APIs.
Whether you are looking for FastAPI training in Mohali, Chandigarh, or Gurdaspur, hands-on classroom cohorts and instructor-led online sessions help you build web services, interactive documentation, and maintainable microservices.
You will move from core Python and asynchronous programming (async/await) to API design, Pydantic validation, and database integration. Work with SQLModel/SQLAlchemy, dependency injection, OAuth2 with JWT, and containerized servers. Learn production deployment concepts for scalable services. Career mentoring and placement assistance support preparation for roles such as Python Developer, FastAPI Backend Engineer, or API Architect.
What is FastAPI Development?
- Asynchronous Architecture: Use Python async and await for concurrency and non-blocking I/O operations.
- Automatic Interactive Documentation: Generate Swagger UI and ReDoc documentation from Python type hints.
- Data Validation & Serialization: Use Pydantic for validation, settings management, and typed data models.
- Robust Dependency Injection: Manage application dependencies, database sessions, and security permissions cleanly.
Core Advantages of a Career in FastAPI Development
- High-speed API development: FastAPI is suited to modern APIs, frontend and mobile integrations, and AI-enabled services.
- Modern backend skills: Build experience relevant to backend engineering, microservices, and cloud systems.
Learn from Proven Backend Practitioners
- Practical instructor-led learning: Training emphasizes API deployment, integration, and performance optimization for learners in Mohali, Chandigarh, and Gurdaspur.
Specialized Allied Technology Modules
- Advanced Python & Asynchronous Programming: Type hints, generators, context managers, async/await, and concurrent task execution.
- Database Design & ORM Integration: PostgreSQL, SQLite, async drivers, SQLAlchemy, and SQLModel.
- DevOps & Cloud Deployment: Docker, Uvicorn, Gunicorn, Nginx, CI/CD, and cloud platforms such as AWS, Heroku, and DigitalOcean.
- API Security & Authentication: OAuth2, JWT, bcrypt password hashing, and CORS middleware.
Core Technical Pillars & Architecture
- FastAPI Core & Routing: APIRouter, dependency injection, middleware, and exception handling.
- Pydantic v2 Mastery: Data models, custom validators, field constraints, and safe request parsing.
- Asynchronous Database Operations: Async SQLAlchemy sessions and database migrations with Alembic.
- Testing & Quality Assurance: Unit and integration tests with pytest and FastAPI TestClient.
- Background Tasks & WebSockets: Background processing and real-time, bidirectional communication.
Advanced FastAPI Engineering: Practical Industry Focus
- Real-World Capstone Projects: E-commerce microservices, real-time chat servers, authentication systems, and REST APIs.
- Secure Architecture First: Production-minded API security and data protection.
- Modern Toolchain Mastery: Python, FastAPI, Pydantic, SQLAlchemy, PostgreSQL, Redis, and Docker.
Dedicated Career Support: Mock interviews, GitHub portfolio reviews, system-design discussions, and placement-drive preparation.
Agentic AI Course
- Day 1: Python refresher, virtual environments → Setup, small scripts
- Day 2: Python functions, OOP, JSON → Parse JSON data
- Day 3: HTTP, APIs, async Python → Call a public API
- Day 4: What is Generative AI, LLMs, tokens, context → Explore chat models
- Day 5: Prompt engineering basics → Prompt templates for 5 tasks
- Day 6: Advanced prompting: few-shot, CoT, role prompts → Prompt library
- Day 7: LLM APIs (Claude/OpenAI) → First API chatbot in Python
- Day 8: Streaming, system prompts, parameters → CLI chatbot with streaming
- Day 9: Structured outputs, JSON schema, Pydantic → Resume parser
- Day 10: Mini Project 1 → Document summarizer tool
- Day 11: Tool/function calling → Calculator and weather tools
- Day 12: Building a basic agent loop (ReAct) → Agent from scratch
- Day 13: Embeddings and semantic search → Embed and search sentences
- Day 14: Vector databases (Chroma/FAISS) → Store and query documents
- Day 15: Document loading, chunking strategies → PDF ingestion pipeline
- Day 16: RAG pipeline → Chat with your PDF
- Day 17: Advanced RAG: reranking, hybrid search → Improve retrieval quality
- Day 18: Mini Project 2 → Company FAQ RAG bot
- Day 19: LangChain basics: chains, prompts, parsers → LCEL chains
- Day 20: LangChain tools and agents → Web search agent
- Day 21: Memory: short-term, long-term, summaries → Chatbot with memory
- Day 22: LangGraph fundamentals: nodes, edges, state → Simple graph workflow
- Day 23: LangGraph: conditional edges, loops → Self-correcting agent
- Day 24: Human-in-the-loop, checkpoints → Approval workflow agent
- Day 25: Planning and reflection patterns → Plan-and-execute agent
- Day 26: Multi-agent concepts → Researcher + writer agents
- Day 27: CrewAI → Content creation crew
- Day 28: AutoGen / OpenAI Agents SDK → Multi-agent conversation
- Day 29: Model Context Protocol (MCP) → Build a simple MCP server
- Day 30: MCP clients and tool integration → Connect agent to MCP tools
- Day 31: Browser/computer-use and code-execution agents → Web scraping agent
- Day 32: Agent for data: SQL and CSV agents → Natural-language data query bot
- Day 33: Evaluation of LLM apps (RAGAS, LangSmith) → Evaluate RAG bot
- Day 34: Guardrails, safety, prompt injection → Add input/output filters
- Day 35: Capstone planning → Architecture and tool design
- Day 36: FastAPI backend for agents → Expose agent as API
- Day 37: Frontend with Streamlit/Gradio → Chat UI for agent
- Day 38: Capstone: core agent logic → Tools + planning
- Day 39: Capstone: RAG + memory → Knowledge layer
- Day 40: Capstone: multi-agent orchestration → Final workflow
- Day 41: Docker and deployment → Containerize app
- Day 42: Cloud deployment (Render/AWS/HF Spaces) → Live deployment
- Day 43: Observability, cost control, caching → Add logging and token tracking
- Day 44: GitHub, README, demo video, resume → Portfolio package
Day 45: Final demo and viva → Present capstone
- Week 1: Python essentials, APIs, async → Scripts and API calls
- Week 2: LLM fundamentals, prompt engineering → Prompt library
- Week 3: LLM APIs, streaming, structured outputs → Chatbot + resume parser
- Week 4: Tool calling and ReAct agent → Agent from scratch
- Week 5: Embeddings, vector DBs → Semantic search engine
- Week 6: RAG pipeline and advanced retrieval → Chat with PDFs
- Week 7: LangChain: chains, tools, memory → Agent with web search + memory
- Week 8: LangGraph: stateful workflows → Self-correcting, human-in-loop agent
- Week 9: Multi-agent: CrewAI, AutoGen, Agents SDK → Research crew
- Week 10: MCP, browser and code agents → MCP server + web agent
- Week 11: Evaluation, guardrails, FastAPI, Streamlit → Evaluated, secured app
Week 12: Capstone, Docker, cloud deploy → Deployed multi-agent product, demo
- Week 1: Python fundamentals → Practice scripts
- Week 2: Python OOP, files, JSON, virtual envs → CLI utilities
- Week 3: APIs, async, Git/GitHub → API client project
- Week 4: ML and LLM basics, transformers overview → Explore Hugging Face models
- Week 5: Prompt engineering (basic to advanced) → Prompt evaluation sheet
- Week 6: LLM APIs, streaming, parameters → Streaming chatbot
- Week 7: Structured outputs, Pydantic, function calling → Data-extraction tool
- Week 8: ReAct and agent loop from scratch → Custom tool-using agent
- Week 9: Embeddings, similarity search → Semantic search app
- Week 10: Vector DBs: Chroma, FAISS, Pinecone → Multi-doc search
- Week 11: RAG fundamentals → PDF Q&A bot
- Week 12: Advanced RAG: hybrid, reranking, query rewriting, GraphRAG → Improve a RAG system
- Week 13: LangChain deep dive → Multi-tool agent
- Week 14: Memory systems → Personalized assistant
- Week 15: LangGraph core → Workflow agent
- Week 16: LangGraph advanced: checkpoints, human-in-loop, subgraphs → Approval-based agent
- Week 17: Planning, reflection, self-critique patterns → Coding/research agent
- Week 18: Multi-agent: CrewAI, AutoGen, Agents SDK → Content/research crew
- Week 19: MCP servers and clients → Custom MCP tools
- Week 20: Browser, computer-use, SQL and data agents → Web automation + data bot
- Week 21: Fine-tuning basics, open-source models (Ollama, local LLMs) → Run local agent
- Week 22: Evaluation, guardrails, security, observability → Eval dashboard
- Week 23: FastAPI, Streamlit/React UI, Docker, cloud deploy → Deployed agent app
Week 24: Final capstone and portfolio → Production-grade agentic product, demo day
Apply here
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