Detect and report data/feature drift between a reference and current dataset using Evidently (Evidently AI)

domain: ml-ops · 6 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗

Steps

  1. Install: pip install evidently
  2. Import the current API surface: from evidently import Dataset, DataDefinition, Report; from evidently.presets import DataDriftPreset
  3. Define a schema mapping column types: schema = DataDefinition(numerical_columns=[...], categorical_columns=[...])
  4. Wrap each dataframe as an Evidently Dataset: current_ds = Dataset.from_pandas(current_df, data_definition=schema); reference_ds = Dataset.from_pandas(reference_df, data_definition=schema)
  5. Build and run the report: report = Report([DataDriftPreset()]); my_eval = report.run(current_ds, reference_ds)
  6. View or export results: render my_eval inline in a notebook, or use my_eval.json() / my_eval.dict() / my_eval.save_html("file.html")

Known gotchas

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