a POC to learn gRPC, and playaround in a imaginary scenario, Where you want the use a ML model in python, but want to expose somesort other BACKEND to clients, for whatever reason:
- productivity
- performance
- security
- legacy
- stubbornness 🙃
altho obviously you could do this over a stupid rest API.
Today I decided to do it over a stupid gRPC API, to reduce request latency, resource utilization and waste an evening.
Some client that
you convinced to use
your backend!
another server or
somesort of UI
┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ SomeClient │ │ Node Backend │ │Python Backend│
└───────┬──────┘ └──────┬───────┘ └───────┬──────┘
│ │ │
│ │ │
│ POST: {transaction_id}│ │
│ /api/FraudActivity │ │
├──────────────────────►│ │
│+[AUTH/PERMISSION] │ rpc: package fraud_detection: FraudDetectionService(PredictFraud)
│ │ ┌────┬─────────┐
│ ├──────────────────────────────┼──► │ │
│ │message PredictionRequest { │ │Load model
│ │ int64 transaction_id │ │ ┼
│ │ double amount │ │ExtractsFeature
│ │ int64 time │ │ ┼
│ │ int32 age_of_account │ │Do Prediction
│ │ int32 number_of_transactions │ │
│ │} │ │ │
│ │ │ │ │
│ │◄─────────────────────────────┼────┤ │
│ │ message PredictionResponse { └────┼─────────┘
│ │ int64 transaction_id │
│ │ bool predicted_label │
│ │ double fraud_probability │
│ │ } │
│◄──────────────────────┤ │
│ │ │
│{ │ │
│ "transaction_id": "Y12a34x56", │
│ "predicted_label": true, │
│ "fraud_probability": 99.99, │
│} │ │
│ │ │
│ │ │
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