1. Model Monitoring
  2. Censius


Sign up for a free account here. If you wish, consult the Censius documentation for more information on how to set up Censius to your liking. Once you have signed up for Censius, you will need to create a new project. You can do this by clicking on the New Project button on the top right corner of the screen. You should note the Project ID as you will need it later.

You will need to register a dataset with Censius which is associated with your model. The dataset contains information about the data used to train your model, including the feature names and targets. Note your dataset must contain a timestamp column. This is used to track the performance of your model over time. Instantiate a Censius Client within your notebook or file and register a dataset in the project you previously created:

from censius import CensiusClient, DatasetType

api_key = "<YOUR_API_KEY>"
tenant_id = "<YOUR_TENANT_ID>"
client = CensiusClient(api_key=api_key, tenant_id=tenant_id)

df = pd.read_csv("SOME_CSV_FILE.csv")
    features= [
        {"name": "feature_1", "type": DatasetType.DECIMAL},
        {"name": "feature_2", "type": DatasetType.DECIMAL},
        {"name": "target", "type": DatasetType.INT}
    timestamp={"name": "Timestamp", "type": DatasetType.UNIX_S}

This should return a dataset ID which you use to add a Censius logger to your Conduit object.

Adding a Censius Logger

Once you have instantiated your Conduit object in code (see here), you can add a Censius logger to it by calling the add_logger method. The Censius logger requires the following platform specific arguments:

  • project_id: The ID of the project you created in Censius.
  • model_type: The type of Censius model you are using. This can be either censius.ModelType.BINARY_CLASSIFICATION or censius.ModelType.REGRESSION.
  • training_info: A dictionary containing the Censius Dataset information. This should contain the following keys:
    • id: The ID of the dataset you registered with Censius.
    • method: Always censius.Dataset.ID
from cerebrium import Conduit, model_type, logging_platform
from censius import Dataset, ModelType
conduit = Conduit(...)

platform_args = {
    "project_id": <PROJECT_ID>,
    "model_type": ModelType.BINARY_CLASSIFICATION,
    "training_info": {
        "id": <DATASET_ID>,
        "method": Dataset.ID

    platform_authentication={"api_key": "<CENSIUS_API_KEY>", "tenant_id": "<CENSIUS_TENANT_ID>"},
    features=["feature_1", "feature_2", "feature_3"],

You can now deploy your Conduit as normal! Your model will log predictions to Censius every time you call the endpoint.