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README.md

README.md

 
 

README.md

How to run MVAD(v1.1) in postman

API Overview

There are 7 APIs provided in Multivariate Anomaly Deteciton:

  • Training: Use Train Model API to create and train a model, then use Get Model Status API to get the status and model metadata.
  • Inference:
    • Use Async Inference API to trigger an asynchronous inference process and use Get Inference results API to get detection results on a batch of data.
    • You could also use Sync Inference API to trigger a detection on one timestamp every time.
  • Other operations: List Model API and Delete Model API are supported in MVAD for model management.
API Name Method Path Description
Train Model POST {endpoint}/anomalydetector/v1.1/multivariate/models Create and train a model
Get Model Status GET {endpoint}anomalydetector/v1.1/multivariate/models/{modelId} Get model status and model metadata with modelId
Async Inference POST {endpoint}/anomalydetector/v1.1/multivariate/models/{modelId}:detect-batch Trigger an asynchronous inference with modelId
Get Inference Results GET {endpoint}/anomalydetector/v1.1/multivariate/detect-batch/{resultId} Get asynchronous inference resulsts with resultId
Sync Inference POST {endpoint}/anomalydetector/v1.1/multivariate/models/{modelId}:detect-last Trigger a synchronous inference with modelId
List Model GET {endpoint}/anomalydetector/v1.1/multivariate/models List all models
Delete Model DELET {endpoint}/anomalydetector/v1.1/multivariate/models/{modelId} Delete model with modelId

Please click this button to fork the API collection: Fork in Postman

  1. Select environment as MVAD.

  2. Select Environment, paste your Anomaly Detector endpoint, key and dataSource url in to the CURRENT VALUE column, click Save to let the variables take effect.

  3. Select Collections, and click on the first API - Create and train a model, then click Send.

    Note: If your data is one CSV file, please set the dataSchema as OneTable, if your data is multiple CSV files in a folder, please set the dataSchema as MultiTable.

  4. In the response of the first API, copy the modelId and paste it in the modelId in Environments, click Save. Then go to Collections, click on the second API - Get model status, and click Send.

  5. Select the third API - Batch Detection, and click Send. This API will trigger an asynchronous inference task, and you should use the Get batch detection results API several times to get the status and the final results.

  6. In the response of the third API, copy the resultId and paste it in the resultId in Environments, click Save. Then go to Collections, click on the fourth API - Get batch detection results, and click Send.

  7. For the rest of the APIs, click on each and click Send to test on their request and response.