An ML API (Application Programming Interface) provides a standardized way for applications to interact with machine learning models, allowing real-time predictions through HTTP requests.
🌐Language agnostic - any client can use the model
🔄Real-time inference for web and mobile apps
📈Scalable deployment with load balancing
🔒Secure access control and authentication
📊Easy monitoring and logging capabilities
API Design Patterns
🌐
REST API
Standard HTTP methods for stateless model inference
📊
GraphQL API
Flexible query language for complex model interactions
⚡
Streaming API
Real-time data processing for continuous predictions
📦
Batch API
Process multiple samples efficiently in bulk
Popular ML API Frameworks
⚡
FastAPI
Modern, fast Python framework with automatic API documentation
Automatic OpenAPI/Swagger docs
Built-in data validation
Async support
Type hints integration
🌶️
Flask
Lightweight and flexible Python micro-framework
Minimal setup required
Highly customizable
Large ecosystem
Easy to learn
🎯
Django REST
Full-featured framework with built-in admin and ORM
Built-in authentication
Database ORM
Admin interface
Robust security
🔥
TorchServe
PyTorch's official serving framework for production