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
Install: pip install evidently
Import the current API surface: from evidently import Dataset, DataDefinition, Report; from evidently.presets import DataDriftPreset
Define a schema mapping column types: schema = DataDefinition(numerical_columns=[...], categorical_columns=[...])
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)
Build and run the report: report = Report([DataDriftPreset()]); my_eval = report.run(current_ds, reference_ds)
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
The per-column drift detection method is chosen automatically based on column type and sample size, and drift checks drop nulls before comparing distributions — run a separate DataSummaryPreset for missing-value/data-quality checks
Overall 'dataset drift' is flagged by default when at least 50% of columns individually show drift; this share threshold and per-column methods/thresholds are configurable but the default matters if you're comparing against someone else's report
Evidently's Python API has changed across major versions (current docs use Dataset/DataDefinition/Report plus presets imported from evidently.presets) — older tutorials/blog posts using a different import layout are for prior releases and won't run as-is
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