Machine Learning API Generator Tool
Machine Learning API Generator
Generate REST API endpoints for your ML models with OpenAPI specification, authentication, and comprehensive documentation.
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Start Free TrialHow to Use the ML API Generator
Define your model's input and output schemas, select API features, and generate production-ready REST API code. The tool creates comprehensive OpenAPI specifications and implementation code for multiple frameworks.
Benefits of Generated ML APIs
Standardized API design ensures consistency across your ML services. Generated APIs include proper error handling, input validation, authentication, and comprehensive documentation that makes integration seamless for developers.
Supported Features
The generator creates APIs with:
- OpenAPI 3.0 specification with interactive documentation
- Input/output validation based on your schema definitions
- Multiple authentication methods including API keys and OAuth 2.0
- Batch prediction endpoints for processing multiple requests
- Health check and metrics endpoints for monitoring
- Async prediction support for long-running model inference
- Model versioning to support A/B testing and gradual rollouts
- Rate limiting and error handling for production stability
Deployment-Ready Code
Generated code includes Docker configurations and deployment scripts. Host your ML APIs on Kloudbean's managed cloud infrastructure for scalable, reliable ML model serving with automatic scaling and monitoring.
Frequently Asked Questions
Q. What input/output schema format should I use?
Use JSON schema format with field names as keys and data types as values. Supported types include "number", "string", "boolean", "array", and "object".
Q. Can I customize the generated API code?
Yes, the generated code is fully customizable. You can modify endpoints, add custom validation, integrate with your ML framework, and extend functionality as needed.
Q. Which authentication method should I choose?
For internal APIs, API keys work well. For customer-facing APIs, OAuth 2.0 provides better security. Bearer tokens are good for microservices communication.
Q. How do I handle different model versions?
Enable model versioning to create endpoints like /v1/predict and /v2/predict. This allows gradual rollouts and A/B testing of model updates.
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