Evidently AI for monitoring in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Evidently AI's primary purpose

What is the main function of Evidently AI in a machine learning workflow?

A To train machine learning models faster using GPU acceleration

B To monitor data and model performance over time to detect drift and anomalies

C To deploy machine learning models as REST APIs automatically

D To generate synthetic data for training machine learning models

💡 Hint

Think about what happens after a model is deployed and needs ongoing checks.

Evidently AI focuses on monitoring data and model quality metrics to detect changes or problems after deployment.

💻 Command Output

intermediate

Evidently AI report generation output

What output will the following Evidently AI Python code produce?

MLOps

from evidently.dashboard import Dashboard
from evidently.tabs import DataDriftTab
import pandas as pd

reference_data = pd.DataFrame({'feature': [1, 2, 3, 4, 5]})
curr_data = pd.DataFrame({'feature': [1, 2, 2, 4, 5]})

dashboard = Dashboard(tabs=[DataDriftTab()])
dashboard.calculate(reference_data, curr_data)
result = dashboard.json()
print(result)

A A runtime error because DataDriftTab requires target data

B A SyntaxError due to missing import statements

C A JSON string summarizing data drift metrics showing slight drift in 'feature'

D An empty JSON string because no drift is detected

💡 Hint

DataDriftTab compares distributions between reference and current data.

The code calculates data drift between two datasets and outputs a JSON report with metrics indicating if drift occurred.

❓ Configuration

advanced

Configuring Evidently AI for model performance monitoring

Which configuration snippet correctly sets up Evidently AI to monitor model prediction quality with a threshold for accuracy drop?

A

from evidently.metric_preset import ClassificationPreset
from evidently import ColumnMapping

column_mapping = ColumnMapping(prediction='pred', target='target')
metrics = ClassificationPreset()
metrics.add_metric('accuracy', threshold=0.8)

B

from evidently.metric_preset import ClassificationPreset
from evidently import ColumnMapping

column_mapping = ColumnMapping(prediction='pred', target='target')
metrics = ClassificationPreset()
metrics.accuracy_threshold = 0.8

C

from evidently.metric_preset import ClassificationPreset
from evidently import ColumnMapping

column_mapping = ColumnMapping(prediction='pred', target='target')
metrics = ClassificationPreset(accuracy_threshold=0.8)

D

from evidently.metric_preset import ClassificationPreset
from evidently import ColumnMapping

column_mapping = ColumnMapping(prediction='pred', target='target')
metrics = ClassificationPreset()
metrics.add_accuracy_threshold(0.8)

💡 Hint

Look for the method that explicitly sets a threshold for accuracy in the preset.

Option D uses the correct method add_accuracy_threshold to set the accuracy threshold in the ClassificationPreset.

❓ Troubleshoot

advanced

Diagnosing Evidently AI dashboard calculation error

You run Evidently AI's dashboard.calculate(reference_data, current_data) but get a KeyError: 'target'. What is the most likely cause?

A The current_data or reference_data is missing the 'target' column required for metrics

B The Python environment is missing the pandas package

C The dashboard object was not initialized with the DataDriftTab tab

D Evidently AI requires the 'target' column to be named 'label' instead

💡 Hint

Check if your data frames contain all columns needed by the metrics.

Many Evidently metrics require a 'target' column to compare predictions against true values. Missing it causes KeyError.

🔀 Workflow

expert

Optimal Evidently AI monitoring workflow integration

Which sequence correctly describes the best workflow to integrate Evidently AI monitoring into a CI/CD pipeline for ML models?

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 3,1,2,4

💡 Hint

Think about the logical order from data preparation to deployment to monitoring.

The correct workflow starts with collecting baseline data, then deploying the model, then collecting production data, and finally running monitoring reports.

Practice

1. What is the main purpose of Evidently AI in ML model monitoring?

easy

A. To clean and preprocess raw data before training

B. To train new machine learning models automatically

C. To deploy ML models to production environments

D. To compare old and new data or predictions to detect changes

Solution

1. Step 1: Understand Evidently AI's role

   Evidently AI is designed to monitor ML models by checking if data or predictions have changed over time.

2. Step 2: Identify the main function

   It compares old and new data or predictions to detect data drift or performance issues.

3. Final Answer:

   To compare old and new data or predictions to detect changes -> Option D

4. Quick Check:

   Monitoring = Comparing data changes [OK]

Hint: Evidently AI checks data changes, not training or deployment [OK]

Common Mistakes:

• Confusing monitoring with training models
• Thinking Evidently deploys models
• Assuming it preprocesses data

2. Which of the following is the correct way to create an Evidently dashboard with tabs for data drift and performance?

easy

A. dashboard = Dashboard(tabs=DataDriftTab(), PerformanceTab())

B. dashboard = Dashboard(tabs=[DataDriftTab(), PerformanceTab()])

C. dashboard = Dashboard(tabs=[PerformanceTab, DataDriftTab])

D. dashboard = Dashboard([DataDriftTab(), PerformanceTab()])

Solution

1. Step 1: Review Evidently dashboard syntax

   The Dashboard class expects a list of tab instances passed as the tabs parameter.

2. Step 2: Check correct instantiation

   Tabs must be instantiated with parentheses and passed as a list to the tabs argument.

3. Final Answer:

   dashboard = Dashboard(tabs=[DataDriftTab(), PerformanceTab()]) -> Option B

4. Quick Check:

   Tabs list with instances = dashboard = Dashboard(tabs=[DataDriftTab(), PerformanceTab()]) [OK]

Hint: Tabs must be instances inside a list assigned to tabs parameter [OK]

Common Mistakes:

• Passing classes instead of instances
• Not using a list for tabs
• Incorrect argument syntax

3. Given the following code snippet, what will be the output type of report.save_html('report.html')?

from evidently.dashboard import Dashboard
from evidently.tabs import DataDriftTab

dashboard = Dashboard(tabs=[DataDriftTab()])
report = dashboard.calculate(reference_data, current_data)
report.save_html('report.html')

medium

A. A new HTML file named 'report.html' is created with the dashboard report

B. An error because save_html() returns a string, not a file

C. The report is printed to the console instead of saved

D. Nothing happens because save_html() is not a valid method

Solution

1. Step 1: Understand save_html() method

   The save_html() method saves the dashboard report as an HTML file to the given path.

2. Step 2: Analyze the code behavior

   Calling report.save_html('report.html') creates a file named 'report.html' containing the report content.

3. Final Answer:

   A new HTML file named 'report.html' is created with the dashboard report -> Option A

4. Quick Check:

   save_html() saves file = A new HTML file named 'report.html' is created with the dashboard report [OK]

Hint: save_html() writes an HTML file, does not print or error [OK]

Common Mistakes:

• Thinking save_html() returns a string
• Expecting console output instead of file
• Assuming save_html() is invalid

4. You wrote this code but get an error: TypeError: 'Dashboard' object is not callable. What is the likely cause?

dashboard = Dashboard(tabs=[DataDriftTab(), PerformanceTab()])
report = dashboard(reference_data, current_data)

medium

A. You forgot to import DataDriftTab

B. Tabs must be strings, not instances

C. You should call dashboard.calculate() instead of dashboard()

D. You need to instantiate PerformanceTab with parameters

Solution

1. Step 1: Identify the error cause

   The error says Dashboard object is not callable, meaning dashboard() is invalid syntax.

2. Step 2: Correct method to generate report

   To get a report, you must call dashboard.calculate(reference_data, current_data), not dashboard().

3. Final Answer:

   You should call dashboard.calculate() instead of dashboard() -> Option C

4. Quick Check:

   Use calculate() method to get report [OK]

Hint: Dashboard object is not callable means missing .calculate() [OK]

Common Mistakes:

• Calling dashboard() directly instead of .calculate()
• Assuming tabs must be strings
• Thinking imports cause this error

5. You want to monitor a model's prediction quality over time using Evidently AI. Which combination of tabs should you include in your dashboard to track data drift and model performance together?

hard

A. DataDriftTab and ClassificationPerformanceTab

B. DataQualityTab and RegressionPerformanceTab

C. DataDriftTab and DataQualityTab

D. RegressionPerformanceTab and ClassificationPerformanceTab

Solution

1. Step 1: Identify tabs for data drift and performance

   DataDriftTab monitors changes in input data distribution. ClassificationPerformanceTab tracks model prediction quality for classification tasks.

2. Step 2: Choose correct combination for monitoring

   To monitor both data drift and model performance for classification, use DataDriftTab and ClassificationPerformanceTab together.

3. Final Answer:

   DataDriftTab and ClassificationPerformanceTab -> Option A

4. Quick Check:

   Data drift + classification performance = DataDriftTab and ClassificationPerformanceTab [OK]

Hint: Match tabs to task: DataDrift + ClassificationPerformance for classification [OK]

Common Mistakes:

• Mixing performance tabs for regression with classification
• Using DataQualityTab instead of DataDriftTab
• Choosing two performance tabs without data drift