Skip to main content

AI Application Development

Full-Stack AI Application Architecture​

  • What you Need to Know

    • Frontend Development for AI Applications

    • Backend API Development

    • Database Integration and Data Management

      • Vector databases for embeddings and similarity search
      • Traditional databases for application data
      • Data pipeline design and ETL processes
      • Resources:

Web Application Development with AI​

  • What you Need to Know

    • Streamlit for Rapid Prototyping

      • Building interactive AI demos and prototypes
      • Custom components and advanced layouts
      • Deployment and sharing strategies
      • Resources:

    • Gradio for ML Model Interfaces

      • Creating user-friendly interfaces for AI models
      • Custom input/output components
      • Integration with Hugging Face Spaces
      • Resources:

    • Custom Web Applications with Flask/Django

Mobile AI Application Development​

  • What you Need to Know

API Design and Integration​

  • What you Need to Know

    • RESTful API Development for AI Services

    • GraphQL for Complex AI Applications

    • Microservices Architecture for AI

User Experience Design for AI Applications​

  • What you Need to Know

    • AI-Specific UX Patterns

      • Designing for uncertainty and probabilistic outputs
      • Progressive disclosure and confidence indicators
      • Feedback loops and user correction mechanisms
      • Resources:

    • Accessibility in AI Applications

Real-Time AI Applications​

  • What you Need to Know

    • WebSocket Integration for Live AI

      • Real-time model inference and streaming
      • Live data visualization and updates
      • Collaborative AI applications
      • Resources:

    • Streaming Data Processing

      • Processing continuous data streams
      • Real-time analytics and decision making
      • Event-driven architecture patterns
      • Resources:

Testing and Quality Assurance​

  • What you Need to Know

    • Unit Testing for AI Applications

    • Integration and End-to-End Testing

      • Testing complete AI application workflows
      • Performance testing and load testing
      • User acceptance testing for AI features
      • Resources:

Data Management and Pipelines​

  • What you Need to Know

    • Data Pipeline Architecture

      • ETL/ELT processes for AI applications
      • Data validation and quality assurance
      • Batch and streaming data processing
      • Resources:

    • Feature Stores and Data Versioning

      • Centralized feature management
      • Data lineage and versioning
      • Feature serving for real-time applications
      • Resources:

Security and Privacy in AI Applications​

  • What you Need to Know

    • AI Application Security

    • Privacy-Preserving AI Techniques

      • Differential privacy implementation
      • Federated learning for distributed AI
      • Data anonymization and pseudonymization
      • Resources:

Performance Optimization and Monitoring​

  • What you Need to Know

    • Application Performance Optimization

      • Caching strategies for AI applications
      • Database query optimization
      • Frontend performance and lazy loading
      • Resources:

    • Monitoring and Observability

Deployment and DevOps for AI Applications​

  • What you Need to Know

    • Containerization and Orchestration

      • Docker containers for AI applications
      • Kubernetes deployment and scaling
      • Container registry and image management
      • Resources:

    • CI/CD for AI Applications

      • Automated testing and deployment pipelines
      • Model versioning and rollback strategies
      • Blue-green and canary deployments
      • Resources:

Ready to Deploy? Continue to Module 5: Production Deployment to master scaling, monitoring, and maintaining AI applications in production environments.