Data Drift Detector

What this demo is about

Data Drift Detector is a interactive browser demo in the AI/ML Workflows track. It helps students explore the main decision, key inputs, and output interpretation in browser mode.

Learning objectives

• Explain the main ai/ml decision that Data Drift Detector is designed to support.
• Change input assumptions and predict how the output should respond before running the demo.
• Interpret the result in plain language, not just as a number, chart, or AI recommendation.

Run mode and expectations

• Supported modes: Browser
• Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

Step 1: Inputs

• Start with the default assumptions, then change one variable at a time so students can isolate cause and effect.
• Treat each input as a lever that changes the scenario, baseline, or business context behind the result.

Step 2: Decision buttons

• Use the main run or simulate action to compute the scenario after inputs are set.
• Use export or reset actions, when present, to compare runs or return to a classroom-safe baseline.

Step 3: Outputs and what to notice

• Read the top-line result first, then look for supporting metrics, tables, or narratives that explain why it changed.
• Students should explain whether the output is descriptive, predictive, simulated, or recommended.
• Look for the reference distribution, current distribution, and drift signal
• Observe which variables drift first and whether the drift is meaningful operationally