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Evidently and Metaflow

Run model evaluation or data drift analysis as Metaflow Flow and save the Evidently metrics in S3, visualizing it with the optional Metaflow UI.
This is a community-contributed integration. Author: Marcello Victorino.
Metaflow is an open-source framework to helps scientists and engineers build and manage real-life data science projects.
You can use this integration to generate Evidently HTML reports and test suites, executed via a Metaflow Flow and visualize it as a Card - using the metaflow-card-html plugin.

Overview

Many machine learning teams use Metaflow to orchestrate the multiple stages of ML lifecycle, such as data preparation, training, deployment, serving predictions, and as a model registry.
If you are already familiar with Metaflow, here is an example on how to integrate it with Evidently to track the quality of data and the data drift.
In this case, Metaflow will orchestrate the execution of the Flow, using Evidently to calculate the metrics/tests and generate the visual report and Metaflow to log the HTML results as an artefact. You can then access the metrics in the Metaflow UI interface - or retrieve it via the cards api.

How it works

With Metaflow, you can organize your Batch process into multiple Flows, such as:
  1. 1.
    TrainingFlow: retrieve data, split into train/test, train multiple models in parallel, identify the best and store it as an artifact
  2. 2.
    ServingFlow: from the latest successful TrainingFlow, retrieves the best model and use it to make predictions on the new data
  3. 3.
    MonitoringFlow: triggered by the ServingFlow, retrieves the data used in each last successful Flow and calculates the desired metrics, such as data quality and data drift, where reference is the data used in the TrainingFlow and current comes from the ServingFlow
Note: Evidently calculates a rich set of metrics and statistical tests. You can choose any of the pre-built reports and test suites to define the type of analysis you’d want to get.
Within every Flow, it is possible to store artifacts that can be visualised with the card feature. This way, you can save the HTML content of the Evidently reports to be visualized with the metaflow-card-html plugin.

Tutorial: Evaluating Data Drift with Metaflow and Evidently

In this example, we will use Evidently to check input features for Data Drift and log and visualize the resulting report with Metaflow.

Step 1. Install Metaflow and Evidently

Evidently is available as a PyPI package:
$ pip install evidently
For more details, refer to the Evidently installation guide.
To install Metaflow, run:
$ pip install metaflow
For more details, refer to the Metaflow documentation.
Install the metaflow-card-html plugin:
$ pip install metaflow-card-html
And any other dependencies, such as scikit-learn.

Step 2. Define the helper function to obtain rendered HTML

We will use the following helper function to simplify obtaining the final fully rendered HTML content for the Evidently reports.
def get_evidently_html(evidently_object) -> str:
"""Returns the rendered EvidentlyAI report/metric as HTML
Should be assigned to `self.html`, installing `metaflow-card-html` to be rendered
"""
import tempfile
with tempfile.NamedTemporaryFile() as tmp:
evidently_object.save_html(tmp.name)
with open(tmp.name) as fh:
return fh.read()

Step 3. Define the Metaflow Flow

The start step is based on the Evidently getting started tutorial, preparing the data to be used in the following steps.
The monitoring_data_quality behaves as a step, due to the @mf.step decorator. The @mf.card(type='html') decorator adds behavior, ensuring the attribute self.html will be stored and properly rendered as HTML in the Card.
import metaflow as mf
class GettingStartedEvidentlyFlow(mf.FlowSpec):
@mf.step
def start(self):
import numpy as np
from sklearn.datasets import fetch_california_housing
data = fetch_california_housing(as_frame=True)
housing_data = data.frame
housing_data.rename(columns={"MedHouseVal": "target"}, inplace=True)
housing_data["prediction"] = housing_data["target"].values + np.random.normal(
0, 5, housing_data.shape[0]
)
self.reference = housing_data.sample(n=5000, replace=False)
self.current = housing_data.sample(n=5000, replace=False)
self.next(self.monitoring_data_quality)
@mf.card(type="html")
@mf.step
def monitoring_data_quality(self):
import os
os.system("pip install evidently --quiet")
from evidently.test_preset import DataStabilityTestPreset
from evidently.test_suite import TestSuite
from tools.helper_functions import get_evidently_html
print("Monitoring: data quality tests")
data_stability = TestSuite(
tests=[
DataStabilityTestPreset(),
]
)
data_stability.run(reference_data=self.reference, current_data=self.current)
self.html = get_evidently_html(data_stability)
self.next(self.end)
@mf.step
def end():
print("Flow completed")
Which can be executed with the command:
$ python <path_to_the_metaflow_script>.py run

Step 4. Visualize the report in the respective Card

The respective card can be visualized in multiple ways, such as via the optional Metaflow UI, the api client, or just simply using the command line interface:
$ python <path_to_the_metaflow_script>.py card view <step_name>
Here is an example of the Card in the Metaflow UI:
Metaflow UI: card html