Logging Predictions and Actuals

The inference data (predictions and actuals) can be logged into truefoundry with the mlfoundry client

Note: Each prediction or actual data packet is associated with a model_version. User needs to pass model_version_fqn with each log request which can be found from the experimentation tracking section of the truefoundry's dashboard.

How to get the model_version_fqn?

A model_version can be found in the experimentation section of the dashboard. Go to the dashboard and click on the project and navigate to the models section, click on the model to view the model versions.

Logging the data

import mlfoundry as mlf

client = mlf.get_client()
data_id = uuid.uuid4().hex

                "feature1": 3.33,
                "feature2": "class1",
                "value": "pred_class1",
                "probabilities": {"pred_class1": 0.2, "pred_class2": 0.8},
                "shap_values": {"feature1": 0.23, "feature2": 0.77},
            raw_data={"data": "any_data"},

    model_version_fqn="", actuals=[mlf.Actual(data_id=data_id, value="pred_class2")]

Prediction Data Packet

The prediction data packet mlf.Prediction has following elements:

  • data_id: A unique data id representing a unique inference event.
  • features: A dictionary of feature names as keys and feature values as values.
  • prediction_data: This comprises of prediction value, prediction_probabilities (only for categorical data), shap_values(optional).
  • occurred_at: timestamp at which inference occured. If not passed it is set to the current timestamp at which prediction is getting logged.
  • raw_data: this gives user the freedom to log any metadata or additional information associated with the inference.

Actual Data Packet

The actual data packet mlf.Actual has following elements:

  • data_id: A unique data id representing a unique inference event for which actual is being logged.
  • value: the actual value for the event.


  • Prediction Data Packet corresponding to a data_id must be logged before logging the actual.
  • Actual value is optional to log. If logged, user can have better visualizations on the monitoring dashboard.
  • The data_id must be maintained and saved by the user (truefoundry platform's user) in order to log the actuals correctly. This task can be cumbersome in a few cases, so it can be dealt with some cases with API described below.

Generating data_id from hash of features

This function generates unique id by from with the help of a hash on features and timestamp (optional).

import mlfoundry as mlf

client = mlf.get_client()
data_id = client.generate_hash_from_data(
        "features1": 1.22,
        "feature2": "class2"
data_id_with_ts = client.generate_hash_from_data(
        "features1": 1.22,
        "feature2": "class2"

This API can be useful for users who do not want to maintain the data_id. The user can log the actuals by generating data_id from the same features and timestamp(optional) that were used at the time of logging predictions.