Enable model monitoring (beta)#

To see tracking results, model monitoring needs to be enabled in each model.

To enable model monitoring, include serving_fn.set_tracking() in the model server.

To utilize drift measurement, supply the train set in the training step.

In this section

Model monitoring demo#

Use the following code blocks to test and explore model monitoring.

# Set project name
project_name = "demo-project"

Deploy model servers#

Use the following code to deploy a model server in the Iguazio instance.

import os
import pandas as pd
from sklearn.datasets import load_iris

from mlrun import import_function, get_dataitem, get_or_create_project
from mlrun.platforms import auto_mount

project = get_or_create_project(project_name, context="./")

# Download the pre-trained Iris model

iris = load_iris()
train_set = pd.DataFrame(iris['data'],
                         columns=['sepal_length_cm', 'sepal_width_cm',
                                  'petal_length_cm', 'petal_width_cm'])

# Import the serving function from the function hub
serving_fn = import_function('hub://v2_model_server', project=project_name).apply(auto_mount())

model_name = "RandomForestClassifier"

# Log the model through the projects API so that it is available through the feature store API
project.log_model(model_name, model_file="model.pkl", training_set=train_set)

# Add the model to the serving function's routing spec
serving_fn.add_model(model_name, model_path=f"store://models/{project_name}/{model_name}:latest")

# Enable model monitoring

# Deploy the function

Simulating requests#

Use the following code to simulate production data.

import json
from time import sleep
from random import choice, uniform

iris_data = iris['data'].tolist()

while True:
    data_point = choice(iris_data)
    serving_fn.invoke(f'v2/models/{model_name}/infer', json.dumps({'inputs': [data_point]}))
    sleep(uniform(0.2, 1.7))