Data drift detection basics in MLOps - Step-by-Step Execution

Process Flow - Data drift detection basics

Collect baseline data

↓

Train model on baseline

↓

Collect new incoming data

↓

Compare new data to baseline

↓

Calculate drift metrics

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Is drift above threshold?

No→Continue monitoring

Yes↓

Trigger alert or retrain model

This flow shows how data drift detection compares new data to baseline data, calculates metrics, and triggers alerts if drift is detected.

Execution Sample

MLOps

baseline_data = [10, 12, 11, 13, 12]
new_data = [10, 15, 11, 14, 20]
differences = [abs(n - b) for n, b in zip(new_data, baseline_data)]
drift = sum(differences) / len(baseline_data)
threshold = 3
alert = drift > threshold
print(alert)

This code calculates a simple average absolute difference between baseline and new data to detect drift and prints if alert is triggered.

Process Table

Step	Action	Calculation	Value	Result
1	Calculate absolute differences	abs(10-10), abs(15-12), abs(11-11), abs(14-13), abs(20-12)	[0, 3, 0, 1, 8]	List of differences
2	Sum differences	0 + 3 + 0 + 1 + 8	12	Total difference
3	Calculate average difference	12 / 5	2.4	Drift metric
4	Compare drift to threshold	2.4 > 3	False	No alert triggered

💡 Drift 2.4 is less than threshold 3, so no alert is triggered.

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	After Step 4
baseline_data	[10,12,11,13,12]	[10,12,11,13,12]	[10,12,11,13,12]	[10,12,11,13,12]	[10,12,11,13,12]
new_data	[10,15,11,14,20]	[10,15,11,14,20]	[10,15,11,14,20]	[10,15,11,14,20]	[10,15,11,14,20]
differences	N/A	[0,3,0,1,8]	[0,3,0,1,8]	[0,3,0,1,8]	[0,3,0,1,8]
total_difference	N/A	N/A	12	12	12
drift	N/A	N/A	N/A	2.4	2.4
threshold	3	3	3	3	3
alert	N/A	N/A	N/A	N/A	False

Key Moments - 3 Insights

Why do we calculate the average difference instead of just the sum?

The average difference normalizes the drift metric by the number of data points, making it easier to compare across datasets of different sizes, as shown in step 3 of the execution_table.

What does it mean if the alert is False even though differences exist?

It means the total drift is not large enough to pass the threshold, so the system considers the data stable, as seen in step 4 where 2.4 is less than 3.

Why do we compare new data to baseline data?

Baseline data represents the original data distribution the model was trained on; comparing new data to it helps detect changes or drift, as shown in step 1 where differences are calculated.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what is the drift value calculated?

A 3

B 12

C 2.4

D False

💡 Hint

Check the 'Value' column at step 3 in the execution_table.

At which step does the system decide if an alert should be triggered?

A Step 2

B Step 4

C Step 1

D Step 3

💡 Hint

Look for the step where drift is compared to threshold in the execution_table.

If the threshold was lowered to 2, what would the alert value be at step 4?

A True

B False

C 12

D 2.4

💡 Hint

Compare drift 2.4 to new threshold 2 in step 4 of execution_table.

Concept Snapshot

Data drift detection compares new data to baseline data.
Calculate a drift metric (e.g., average absolute difference).
Set a threshold to decide if drift is significant.
If drift > threshold, trigger alert or retrain.
This helps keep ML models accurate over time.

Full Transcript

Data drift detection basics involve comparing new incoming data to the original baseline data used to train a model. We calculate a drift metric, such as the average absolute difference between the new and baseline data points. This metric is then compared to a set threshold. If the drift exceeds the threshold, it indicates that the data distribution has changed significantly, and an alert is triggered to notify that the model may need retraining. This process helps maintain model accuracy by detecting when the data environment changes.