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MLOps Academy · Lesson

Set Drift Thresholds and Triggers

Decide when drift should kick off a retrain.

Set Drift Thresholds and Triggers is a free MLOps Academy lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the MLOps Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.

From Detecting to Deciding

Measuring drift is only half the job. The real value is deciding when a shift is big enough to act. That means setting thresholds and triggers. 🚦

What a Threshold Is

A threshold is the line a drift metric must cross before you react. Below it you stay calm; above it you investigate or retrain.

Start From Known Rules

A sensible first threshold is PSI above 0.25 per feature, since that is the common significant-drift line. Tune it later with your own data.

PSI_THRESHOLD = 0.25

def is_drifted(psi_value):
    return psi_value > PSI_THRESHOLD

One Feature or Many

Decide your unit of alarm. Trigger on a single critical feature, or on the share of features that drifted, like more than 30% of all columns.

share = drifted_count / total_features
trigger = share > 0.3

Avoid Trigger-Happy Alerts

One noisy day should not fire a retrain. Require drift to persist across a window, say three checks in a row, before you pull the trigger.

Severity Tiers

Not all drift deserves the same response. Use tiers: a warning that just notifies, and a critical level that actually kicks off retraining.

if psi_value > 0.25:
    level = "critical"
elif psi_value > 0.1:
    level = "warning"
else:
    level = "ok"

Connect to a Trigger

A trigger is the action wired to a breach. It might open an alert, call a webhook, or start your training pipeline run.

if level == "critical":
    start_retraining_pipeline()

Prefer Performance When You Can

Input drift is a proxy. If labels arrive, trigger on a real metric drop, like accuracy under target, which is far more trustworthy than input shift alone.

Add a Cooldown

After a retrain, wait before allowing the next trigger. A cooldown stops the system from retraining in a loop while metrics settle.

Calibrate on History

Do not guess thresholds blindly. Replay past data to see what your thresholds would have fired, then adjust until alerts match real problems. 🔧

Keep Humans Informed

Even when triggers run automatically, log every decision and notify the team. Good observability keeps automation trusted instead of mysterious. 🔔

Quick Check

One question on keeping alerts sane.

Recap

You turned drift signals into action: set a threshold, use windows and tiers to cut noise, prefer real metrics, add a cooldown, and always keep humans in the loop. 🎯

Frequently asked questions

Is the “Set Drift Thresholds and Triggers” lesson free?

Yes — the full text of “Set Drift Thresholds and Triggers” is free to read here on the web, and the MLOps Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the MLOps Academy course, upgrade to CoddyKit PRO.

What will I learn in “Set Drift Thresholds and Triggers”?

Decide when drift should kick off a retrain. You practise MLOps Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.

Do I need any experience to start MLOps Academy?

No prior experience is required. MLOps Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.

How long does the “Set Drift Thresholds and Triggers” lesson take?

Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.

Can I write and run code in this MLOps Academy lesson?

Yes. Every MLOps Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.

All lessons in this course

  1. Data Drift vs Concept Drift
  2. Measure Drift with PSI and KS
  3. Generate Drift Reports with Evidently
  4. Set Drift Thresholds and Triggers
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