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POST
/
batch_predict
Batch Prediction
curl --request POST \
  --url https://api.example.com/batch_predict \
  --header 'Content-Type: application/json' \
  --data '
{
  "records": [
    {}
  ]
}
'
{
  "predictions": [
    {}
  ]
}

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Batch Purchase Predictions

Processes multiple student records in a single request for efficient bulk predictions.

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Endpoint

POST /batch_predict

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Request Body

​
records
array
required
Array of student engagement records. Must contain at least 1 record.Validation: min_length=1Each record must be a valid PredictRequest object.

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Response

Returns an array of predictions matching the order of input records.
​
predictions
array
required
Array of prediction results, one for each input record in the same order.

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Status Codes

  • 200 OK - Batch prediction successful
  • 422 Unprocessable Entity - Validation error in one or more records
  • 503 Service Unavailable - Model not loaded

​
Example Request

cURL
curl -X POST "http://localhost:8000/batch_predict" \
  -H "Content-Type: application/json" \
  -H "accept: application/json" \
  -d '{
    "records": [
      {
        "student_country": "United States",
        "days_on_platform": 45,
        "minutes_watched": 320.5,
        "courses_started": 3,
        "practice_exams_started": 5,
        "practice_exams_passed": 3,
        "minutes_spent_on_exams": 87.2
      },
      {
        "student_country": "Canada",
        "days_on_platform": 12,
        "minutes_watched": 45.0,
        "courses_started": 1,
        "practice_exams_started": 0,
        "practice_exams_passed": 0,
        "minutes_spent_on_exams": 0.0
      },
      {
        "student_country": "United Kingdom",
        "days_on_platform": 90,
        "minutes_watched": 1250.0,
        "courses_started": 8,
        "practice_exams_started": 12,
        "practice_exams_passed": 10,
        "minutes_spent_on_exams": 340.5
      }
    ]
  }'

​
Example Response

200 OK
{
  "predictions": [
    {
      "predicted_purchase_probability": 0.7834,
      "predicted_purchase": 1
    },
    {
      "predicted_purchase_probability": 0.2145,
      "predicted_purchase": 0
    },
    {
      "predicted_purchase_probability": 0.9512,
      "predicted_purchase": 1
    }
  ]
}

​
Error Responses

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Validation Error

422 Unprocessable Entity
{
  "detail": "practice_exams_passed cannot exceed practice_exams_started."
}
This error occurs when any record violates the business rule constraint.

​
Empty Records Array

422 Unprocessable Entity
{
  "detail": [
    {
      "loc": ["body", "records"],
      "msg": "ensure this value has at least 1 items",
      "type": "value_error.list.min_items"
    }
  ]
}

​
Service Unavailable

503 Service Unavailable
{
  "detail": "Model is not loaded."
}

​
Implementation Details

Defined in src/api.py:292-297 Request Model: BatchPredictRequest (src/api.py:42-43) 
class BatchPredictRequest(BaseModel):
    records: List[PredictRequest] = Field(..., min_length=1)
Response Model: BatchPredictResponse (src/api.py:46-47) 
class BatchPredictResponse(BaseModel):
    predictions: List[PredictResponse]

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Performance Considerations

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Vectorized Processing

The endpoint uses pandas DataFrame operations and NumPy for efficient batch processing:
  1. Single DataFrame conversion - All records converted to DataFrame once
  2. Vectorized validation - Business rules checked across all records simultaneously
  3. Batch feature engineering - Features computed for all records in parallel
  4. Batch prediction - Model inference runs on entire batch
This approach is significantly faster than calling /predict multiple times.

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Monitoring Updates

All predictions are tracked atomically with thread-safe locking:
  • Feature distributions updated for drift detection
  • Prediction rates tracked for model performance monitoring
  • Individual predictions logged to artifacts/prediction_log.jsonl

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Use Cases

  • Daily scoring jobs - Score all active students each morning
  • Campaign targeting - Identify high-probability purchasers for email campaigns
  • Cohort analysis - Analyze purchase propensity across student segments
  • A/B testing - Generate predictions for experimental groups

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