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AI Full Stack Development

This module trains learners to build end-to-end AI-powered web applications by integrating modern frontend frameworks, backend APIs, and deployed AI models. It covers full-stack development, DevOps, real-time analytics, third-party API integration, and scalable deployment, culminating in a production-ready AI application.

Day 1-15: Overview of AI Full Stack Architecture​

Topics Covered​

  • Full‐Stack Integration:
    • Architectural overview of a system integrating a modern frontend, backend APIs, and AI model serving.
  • Challenges:
    • Data synchronization, latency across components, and security between client and server.
  • Environment Setup:
    • Establishing Docker‐based environments for both frontend and backend with CI/CD.

Hands‐on Tasks​

  • Create a project scaffold incorporating a React/Vue frontend and a FastAPI/Express backend.
  • Configure Docker Compose to launch all services.

Deliverables​

  • A summary report detailing the architecture, challenges, and design considerations.
  • A public GitHub repository with the initial project scaffold and CI/CD pipelines.

Day 16-30: Frontend Development Essentials​

Topics Covered​

  • Modern UI Frameworks:
    • Mastering React, Vue, or Angular; responsive design, component-based architecture, and state management (Redux/Vuex).
  • AI Components:
    • Integrating simple AI features like chatbots or recommendation widgets via REST API.
  • Challenges:
    • Managing asynchronous data fetching and ensuring smooth user experiences.

Hands‐on Tasks​

  • Develop a small frontend application that consumes an AI inference API.
  • Implement responsive layouts and state management.

Deliverables​

  • A tutorial project with complete source code demonstrating AI component integration.
  • A public blog post with code snippets, UI design considerations, and a live demo URL.

Day 31–45: Backend API Development for AI Services​

Topics Covered​

  • API Design:
    • Building secure, scalable RESTful APIs (or GraphQL endpoints) to serve AI model predictions.
  • Microservices Integration:
    • Designing the backend to support multiple AI services concurrently.
  • Challenges:
    • Ensuring error handling, rate limiting, and secure communication.

Hands‐on Tasks​

  • Implement backend API endpoints using FastAPI (or Express) with detailed error handling and authentication.
  • Write comprehensive unit and integration tests.

Deliverables​

  • A complete backend API repository with documented endpoints.
  • Detailed research documentation and a public blog post outlining design choices and security measures.

Day 46–60: AI Model Integration​

Topics Covered​

  • Model Serving:
    • Deploying pre‐trained models using TensorFlow, PyTorch, or HuggingFace.
  • Integration:
    • Connecting backend endpoints to AI model inference pipelines.
  • Challenges:
    • Minimizing inference latency and handling error cases in model serving.

Hands‐on Tasks​

  • Deploy a pre‐trained AI model (e.g., image classifier or text generator) and integrate it with the backend.
  • Create API endpoints that trigger inference and return results.

Deliverables​

  • A demo project with integrated AI model serving, including detailed architecture diagrams.
  • A step‐by‐step blog post with code examples and performance benchmarks.

Day 61–75: Data Management & Pipeline Integration​

Topics Covered​

  • Database Architectures:
    • Designing systems to handle both structured and unstructured data.
  • Data Pipelines:
    • Ingesting, processing, and analyzing data in real‐time using Apache Kafka, AWS Kinesis, etc.
  • Challenges:
    • Ensuring data consistency and real‐time analytics.

Hands‐on Tasks​

  • Develop data pipelines that integrate with the backend and store data in databases (e.g., PostgreSQL, MongoDB).
  • Set up real‐time analytics dashboards.

Deliverables​

  • A research report and public documentation detailing the data architecture.
  • Sample code in a public GitHub repository demonstrating data ingestion and processing pipelines.

Day 76–90: Advanced AI Features & Real‐Time Analytics​

Topics Covered​

  • Performance Dashboards:
    • Implementing Grafana and Prometheus to monitor AI model performance and user interactions.
  • Real‐Time Analytics:
    • Integrating feedback loops to optimize model performance.
  • Challenges:
    • Capturing low‐latency metrics and troubleshooting anomalies.

Hands‐on Tasks​

  • Integrate performance dashboards into the full‐stack application.
  • Develop scripts to log and visualize key metrics.

Deliverables​

  • Code demos with integrated dashboards and analytics.
  • A research paper and public blog post outlining the integration process and performance metrics.

Day 91–105: Security & Authentication​

Topics Covered​

  • Security Best Practices:
    • Implementing OAuth2.0, JWT, and HTTPS for secure communication.
  • Data Protection:
    • Securing user data, model endpoints, and API access.
  • Challenges:
    • Preventing common vulnerabilities such as token theft and man‐in‐the‐middle attacks.

Hands‐on Tasks​

  • Develop a secure full‐stack application demo with authentication flows and encrypted communication.

Deliverables​

  • A secure demo application with complete source code.
  • Detailed documentation and a blog post on security protocols and implementation.

Day 106–120: DevOps for AI Full Stack​

Topics Covered​

  • CI/CD Pipelines:
    • Automating testing, building, and deployment for both frontend and backend.
  • Container Orchestration:
    • Using Docker and Kubernetes to manage scalable deployments.
  • Challenges:
    • Maintaining a consistent deployment workflow across multiple services.

Hands‐on Tasks​

  • Set up end‐to‐end CI/CD pipelines using GitHub Actions or Jenkins.
  • Document the deployment process and test automation.

Deliverables​

  • A fully automated CI/CD pipeline demonstration with test reports and deployment scripts.
  • Architecture diagrams and detailed public documentation on the DevOps process.

Day 121–135: Scaling Full Stack AI Applications​

Topics Covered​

  • Scaling Strategies:
    • Techniques for load balancing, caching, horizontal scaling, and database optimization.
  • Challenges:
    • Handling high concurrency and ensuring low latency across services.

Hands‐on Tasks​

  • Implement auto‐scaling and caching in the full‐stack application.
  • Run performance benchmarks to validate scaling improvements.

Deliverables​

  • A comprehensive research report with architecture diagrams (Draw.io) showing scaling strategies.
  • Sample code demonstrating effective scaling, published in a public case study.

Day 136–150: Integration with Third‐Party APIs​

Topics Covered​

  • External Services:
    • Incorporating payment gateways, social media, analytics, and cloud APIs.
  • Challenges:
    • Handling API rate limits, authentication, and data format conversions.

Hands‐on Tasks​

  • Integrate one or more third‐party APIs into the full‐stack application.
  • Document the integration process and error handling strategies.

Deliverables​

  • A code demo showcasing third‐party API integration.
  • A public blog tutorial with detailed technical documentation and best practices.

Day 151–165: Final Project Planning​

Topics Covered​

  • Collaborative Design:
    • Drafting a comprehensive PRD with feature lists, timelines, and risk management strategies.
  • Challenges:
    • Aligning project objectives across frontend, backend, and AI components.

Hands‐on Tasks​

  • Organize group sessions to finalize project requirements and create detailed architecture diagrams.

Deliverables​

  • A complete project proposal document, feature report, and architecture diagrams (Draw.io).
  • Public presentation slides summarizing the project plan.

Day 166–180: Final Project Implementation & Deployment​

Topics Covered​

  • Integration & Testing:
    • End‐to‐end development, comprehensive integration testing, and final deployment.
  • Continuous Deployment:
    • Leveraging CI/CD pipelines for live production deployment.

Hands‐on Tasks​

  • Develop, test, and deploy the complete full‐stack application.
  • Conduct performance and security audits, and document all test results.

Deliverables​

  • A fully functional AI full‐stack application demo with public access.
  • Comprehensive testing reports, final project documentation, and a live deployment demonstration.

Tech Stack​

  • Frontend:
    • React, Vue, or Angular; HTML; CSS; JavaScript
  • Backend:
    • Python (FastAPI, Flask), Node.js (Express)
  • Databases:
    • PostgreSQL, MongoDB, Redis
  • AI Frameworks:
    • TensorFlow, PyTorch, HuggingFace
  • Deployment & DevOps:
    • Docker, Kubernetes, AWS Digital Ocean, GitHub Actions, Jenkins
  • Monitoring:
    • Prometheus, Grafana
  • Documentation:
    • Draw.io, Markdown