Evidently AI for monitoring in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import Evidently's Report class.

MLOps

from evidently.report import [1]

Drag options to blanks, or click blank then click option'

ADashboard

BReport

CMonitor

DAnalyzer

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a non-existent class like Dashboard or Monitor.

The Report class is used to create monitoring reports in Evidently.

2fill in blank

medium

Complete the code to add a data drift metric to the Evidently report.

MLOps

from evidently.metric_preset import [1]

report = Report(metrics=[DataDriftPreset()])

Drag options to blanks, or click blank then click option'

ADataDriftPreset

BClassificationPreset

CDataQualityPreset

DRegressionPreset

Attempts:

3 left

💡 Hint

Common Mistakes

Using presets for quality or model performance instead of data drift.

DataDriftPreset is the preset metric to detect data drift in Evidently reports.

3fill in blank

hard

Fix the error in the code to run the Evidently report on current and reference data.

MLOps

report.run(current_data=[1], reference_data=ref_data)

Drag options to blanks, or click blank then click option'

Acurrent_df

Bcurrent_data

Cdata_current

Dref_data

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the reference data variable as current data.

The variable holding the current dataset must be passed correctly. Here, current_df is the correct variable name.

4fill in blank

hard

Fill both blanks to save the Evidently report as an HTML file.

MLOps

with open([1], [2]) as f:
    f.write(report.as_html())

Drag options to blanks, or click blank then click option'

A'report.html'

B'w'

C'r'

D'report.json'

Attempts:

3 left

💡 Hint

Common Mistakes

Using read mode 'r' or wrong file extensions like .json.

To save the report as HTML, open a file with write mode 'w' and a filename ending with .html.

5fill in blank

hard

Fill all three blanks to create a monitoring dashboard with Evidently and save it as HTML.

MLOps

from evidently.dashboard import [1]
from evidently.dashboard.tabs import [2]

dashboard = Dashboard(tabs=[[3]()])

Drag options to blanks, or click blank then click option'

ADashboard

BDataDriftTab

DReport

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing Report with Dashboard or using wrong tab names.

The Dashboard class is imported from evidently.dashboard, and the DataDriftTab tab is imported from evidently.dashboard.tabs. The dashboard is created with the DataDriftTab instance.

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