Data Drift vs Concept Drift
Tell apart shifting inputs and shifting targets.
Data Drift vs Concept Drift is a free MLOps Academy lesson on CoddyKit — lesson 1 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.
Two Ways the World Shifts
Your model was trained on a snapshot of the past. When reality moves on, it can quietly go stale. There are two distinct ways this happens: data drift and concept drift. 🌍
What Data Drift Means
Data drift happens when the input data changes shape over time. The features your model sees now no longer look like the features it learned from.
A Data Drift Example
A loan model trained pre-pandemic suddenly sees very different incomes and balances. The inputs shifted, even though what they mean has not. That is pure data drift.
What Concept Drift Means
Concept drift is sneakier: the inputs may look the same, but the relationship between inputs and the target has changed under your feet.
A Concept Drift Example
A spam filter sees normal-looking emails, but spammers invent new tricks. The same words now mean something different, so the input-to-label mapping shifted. That is concept drift.
The Key Difference
Data drift changes P(X), the distribution of inputs. Concept drift changes P(y given X), the mapping from inputs to the label. Tell them apart by asking which part moved. 🔑
Why It Matters for You
Data drift can hurt accuracy, but sometimes the model still copes. Concept drift almost always hurts, because the truth itself has moved away from what you learned.
Detecting Each Type
You can spot data drift by watching input distributions, with no labels needed. Concept drift usually needs outcomes, since you must compare predictions against real results.
Naming Your Inputs
It helps to keep a clean reference of your training inputs versus current ones. A tiny config makes drift checks easy to wire up later.
reference = train_df[["income", "balance", "age"]]
current = live_df[["income", "balance", "age"]]
features = list(reference.columns)Label Drift Too
A close cousin is label drift: the mix of target classes changes, like fraud jumping from 1% to 5%. It can signal either data or concept drift behind the scenes.
Drift Is Normal, Not Rare
Drift is not a freak event; it is the default state of any live model. Your job is not to prevent it but to detect it early and respond on time. ✅
Quick Check
Let us make sure you can tell the two apart.
Recap
Two kinds of shift: data drift moves the inputs, concept drift moves the meaning. Ask which part changed, and remember drift is normal, so always be ready to detect it. 🎯
Frequently asked questions
Is the “Data Drift vs Concept Drift” lesson free?
Yes — the full text of “Data Drift vs Concept Drift” 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 “Data Drift vs Concept Drift”?
Tell apart shifting inputs and shifting targets. 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 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Data Drift vs Concept Drift” 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
- Data Drift vs Concept Drift
- Measure Drift with PSI and KS
- Generate Drift Reports with Evidently
- Set Drift Thresholds and Triggers