Data drift detection basics in MLOps - Commands & Configuration

Introduction

Data drift detection helps you notice when the data your machine learning model sees changes over time. This is important because changes in data can make your model less accurate or reliable.

When your model is running in production and you want to check if new data is different from training data.

When you want to alert your team if the input data changes unexpectedly.

When you want to decide if your model needs retraining because the data has shifted.

When monitoring data quality to keep your predictions trustworthy.

When comparing data from different time periods to spot trends or issues.

Commands

This command installs the Evidently library, which helps detect data drift easily in Python.

Terminal

pip install evidently

Expected Output

Collecting evidently

  Downloading evidently-0.3.39-py3-none-any.whl (123 kB)

Installing collected packages: evidently

Successfully installed evidently-0.3.39

Runs a Python script that loads reference and current data, then checks for data drift using Evidently.

Terminal

python detect_drift.py

Expected Output

Data drift detected: True

Drift score: 0.35

Key Concept

If you remember nothing else from this pattern, remember: detecting data drift early helps keep your model accurate and trustworthy.

Code Example

MLOps

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

# Load reference data (training data)
reference_data = pd.read_csv('reference_data.csv')

# Load current data (new incoming data)
current_data = pd.read_csv('current_data.csv')

# Create a dashboard to check data drift
dashboard = Dashboard(tabs=[DataDriftTab()])
dashboard.calculate(reference_data, current_data)

# Save the report as an HTML file
dashboard.save('data_drift_report.html')

# Simple drift detection boolean
from evidently.metrics import DataDriftMetric
metric = DataDriftMetric()
result = metric.calculate(reference_data, current_data)
print(f"Data drift detected: {result['metrics']['dataset_drift']}")
print(f"Drift score: {result['metrics']['drift_score']}")

Common Mistakes

Not comparing current data to a proper reference dataset.

Without a good baseline, the drift detection will be meaningless or misleading.

Always use a clean, representative dataset from training or a stable period as your reference.

Ignoring data preprocessing differences between reference and current data.

Differences in data format or cleaning can look like drift but are just processing mismatches.

Apply the same preprocessing steps to both datasets before drift detection.

Summary

Install the Evidently library to help detect data drift in Python.

Load your reference (training) and current (new) datasets for comparison.

Use Evidently's dashboard and metrics to identify if data drift has occurred.

Early detection of data drift helps maintain model accuracy and trust.

Always preprocess data consistently and use a good reference dataset.

Practice

1. What is the main purpose of data drift detection in machine learning?

easy

A. To check if new data differs significantly from the training data

B. To improve the speed of model training

C. To reduce the size of the training dataset

D. To increase the number of features in the model

Solution

1. Step 1: Understand data drift concept

   Data drift detection is about monitoring if new incoming data changes compared to the data used to train the model.

2. Step 2: Identify the purpose

   This helps ensure the model stays accurate by alerting when data changes too much.

3. Final Answer:

   To check if new data differs significantly from the training data -> Option A

4. Quick Check:

   Data drift = detecting data changes [OK]

Hint: Data drift means new data differs from old data [OK]

Common Mistakes:

• Confusing data drift with model training speed
• Thinking data drift reduces dataset size
• Believing data drift adds features

2. Which of the following is a correct Python code snippet to check data drift using the Kolmogorov-Smirnov test on two datasets data_train and data_new?

easy

A. from scipy.stats import ks_test
result = ks_test(data_train, data_new)
print(result.pvalue)

B. from scipy.stats import ks_2samp
result = ks_2samp(data_train, data_new)
print(result.pvalue)

C. from sklearn.drift import ks_test
result = ks_test(data_train, data_new)
print(result.pvalue)

D. import stats
result = stats.ks_test(data_train, data_new)
print(result.pvalue)

Solution

1. Step 1: Identify correct import and function

   The Kolmogorov-Smirnov test is in scipy.stats as ks_2samp.

2. Step 2: Check function usage

   Calling ks_2samp(data_train, data_new) returns a result with pvalue attribute.

3. Final Answer:

   from scipy.stats import ks_2samp result = ks_2samp(data_train, data_new) print(result.pvalue) -> Option B

4. Quick Check:

   Correct function and import = from scipy.stats import ks_2samp result = ks_2samp(data_train, data_new) print(result.pvalue) [OK]

Hint: Use scipy.stats.ks_2samp for data drift test [OK]

Common Mistakes:

• Using wrong module or function name
• Trying to import non-existent ks_test
• Confusing sklearn with scipy for this test

3. Given the following Python code to detect data drift, what will be the output if data_train = [1, 2, 3, 4, 5] and data_new = [1, 2, 3, 4, 10]?

from scipy.stats import ks_2samp
result = ks_2samp(data_train, data_new)
print(round(result.pvalue, 2))

medium

A. 0.87

B. 0.05

C. 0.01

D. 1.00

Solution

1. Step 1: Understand the test and data

   The Kolmogorov-Smirnov test compares distributions. Here, only one value differs (5 vs 10).

2. Step 2: Interpret p-value meaning

   A high p-value (close to 1) means no significant difference, low means drift detected.

3. Final Answer:

   0.87 -> Option A

4. Quick Check:

   Small difference gives high p-value = 0.87 [OK]

Hint: Small data changes give high p-value (no drift) [OK]

Common Mistakes:

• Assuming any difference means low p-value
• Confusing p-value with test statistic
• Rounding errors in output

4. You wrote this code to detect data drift but get an error: AttributeError: module 'scipy.stats' has no attribute 'ks_test'. What is the fix?

import scipy.stats as stats
result = stats.ks_test(data_train, data_new)
print(result.pvalue)

medium

A. Use stats.kstest instead of ks_test

B. Import ks_test from scipy.stats explicitly

C. Change ks_test to ks_2samp in the code

D. Update scipy package to latest version

Solution

1. Step 1: Identify the error cause

   The error says ks_test does not exist in scipy.stats.

2. Step 2: Use correct function name

   The correct function for two-sample KS test is ks_2samp, not ks_test.

3. Final Answer:

   Change ks_test to ks_2samp in the code -> Option C

4. Quick Check:

   Function name must be ks_2samp [OK]

Hint: Use ks_2samp, not ks_test, for two-sample KS test [OK]

Common Mistakes:

• Trying to import non-existent ks_test
• Using one-sample test function by mistake
• Ignoring error message details

5. You want to monitor data drift for multiple features in your dataset. Which approach best helps detect drift over time and alert you when it happens?

hard

A. Ignore data drift and focus on model accuracy metrics only

B. Retrain the model daily without checking data changes

C. Increase the model complexity to handle any data changes automatically

D. Run a statistical test like KS test on each feature periodically and trigger alerts if p-value is below threshold

Solution

1. Step 1: Understand monitoring multiple features

   Checking each feature for drift helps catch changes in data distribution over time.

2. Step 2: Use statistical tests and alerts

   Applying tests like KS test periodically and alerting on low p-values ensures timely detection.

3. Final Answer:

   Run a statistical test like KS test on each feature periodically and trigger alerts if p-value is below threshold -> Option D

4. Quick Check:

   Periodic tests + alerts = best drift monitoring [OK]

Hint: Test features regularly and alert on low p-values [OK]

Common Mistakes:

• Retraining blindly without drift checks
• Ignoring drift and trusting accuracy alone
• Assuming complex models fix drift automatically