🔌 API Design for ML Models
Build robust, scalable APIs for serving machine learning models
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Model Deployment Pipeline
Introduction to ML API Design
🎯 Why API Design Matters
Your ML model is only useful if applications can interact with it effectively. A well-designed API makes integration seamless, handles errors gracefully, and scales with demand. Good API design considers latency, versioning, authentication, and provides clear contracts for clients.
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Key Insight
The API is the interface between your model and the world. Design it for developers.
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Performance
Low latency and high throughput
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Documentation
Clear schemas and examples
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Security
Auth, rate limiting, validation
🏗️ API Design Principles
1
RESTful Design
Use standard HTTP methods and status codes
2
Versioning
Support multiple API versions for backward compatibility
3
Error Handling
Provide meaningful error messages and codes
4
Consistency
Uniform naming, structure, and response formats
✅ Good APIs
- •Self-documenting endpoints
- •Predictable response formats
- •Proper HTTP status codes
- •Input validation & sanitization
❌ Bad APIs
- •Vague error messages
- •Inconsistent naming
- •No version control
- •Missing authentication