Add metadata to a Repository

The metadata.json can be used to provide metadata about your Deployments. The metadata.json can be provided in the root of your Repository (or the selected contract folder via the contractPath attribute), like the structure below.

repository (or contract path)
|__ metadata.json
...

Types of metadata are supported, of which an example can be found in our Public Scikit-Learn Census Repository:

• features: An array of objects to provide information about the features in the outcome of your model. A feature consists of a name of type string, and an observedMin and observedMax of type number. Extract the latter from your training data. The order of provided features goes hand in hand with the order of the values. E.g. if 10 values are expected, 10 features can be provided, where the first feature corresponds with the first value. features are used to:
  • Display feature names in SHAP explainers
  • Display boundaries in the Input Validation graph, and threshold suggestions when creating input validation alerts
• predictionClasses: An object with keys and values to provide information about the predictions of your model. Every key in the object provides a category name for the corresponding model outcome. In the example, a model outcome of true means the person is credit worthy, so the key is "credit worthy". This category name is then used to provide labels to our monitoring graph, and will also be displayed in the Deployment details.
• problemType: The key problemType expects a string with value classification or regression. This value is used to calculate the right performance metric based on the type of machine learning problem you model is solving. If the problemType is "classification", the performance is calculated as accuracy. If the problemType is "regression", the performance is calculated as RMSE.
• exampleInput: An example of a json input that can be consumed by the model. The exampleInput can be used as a default option to interact with your model in the Test page of the Deployment.
• inputTensorShape: The tensor dimensions of the json input that can be consumed by the model. More information about the tensor shape can be found in the TensorFlow Documentation.
• exampleOuput: An example of a json output that can be given back by the model.
• outputTensorShape: The tensor dimensions of the json output that can be given back by the model. More information about the tensor shape can be found in the TensorFlow Documentation.
• customId: A custom ID that can be attached to the request, used to handle and filter all the predictions in a better way. Find out more about custom ID's.

Example metadata.json file

{
  "features": [
    {
      "name": "Age",
      "observedMin": 18,
      "observedMax": 96
    },
    {
      "name": "Work class",
      "observedMin": 0,
      "observedMax": 7
    },
    {
      "name": "Education",
      "observedMin": 0,
      "observedMax": 15
    },
    {
      "name": "Maritual status",
      "observedMin": 0,
      "observedMax": 6
    },
    {
      "name": "Occupation",
      "observedMin": 0,
      "observedMax": 13
    },
    {
      "name": "Relationship",
      "observedMin": 0,
      "observedMax": 5
    },
    {
      "name": "Race",
      "observedMin": 0,
      "observedMax": 4
    },
    {
      "name": "Sex",
      "observedMin": 0,
      "observedMax": 1
    },
    {
      "name": "Capital gain",
      "observedMin": 93,
      "observedMax": 129014
    },
    {
      "name": "Capital loss",
      "observedMin": 0,
      "observedMax": 14194
    },
    {
      "name": "Hours per week",
      "observedMin": 3,
      "observedMax": 64
    },
    {
      "name": "Native country",
      "observedMin": 0,
      "observedMax": 40
    }
  ],
  "predictionClasses": {
    "Credit worthy": true,
    "Not credit worthy": false
  },
  "problemType": "classification",
  "exampleInput": {
    "instances": [
      [
        51.0,
        0.0,
        13.0,
        2.0,
        4.0,
        1.0,
        0.0,
        1.0,
        12250.0,
        500.0,
        40.0,
        21.0
      ]
    ]
  },
  "inputTensorShape": "(1, 12)",
  "exampleOutput": {
    "predictions": [
      true
    ]
  },
  "outputTensorShape": "(1)",
  "customId": "clientId"
}