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Documentation Index

Fetch the complete documentation index at: https://handbook.fiddler.ai/llms.txt

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Baseline computation strategies for data drift detection in Fiddler. Baseline types determine how reference data is defined and used for comparison with production model behavior. Fiddler supports static and rolling baselines, each serving different monitoring needs and use cases. Static Baselines:
  • Fixed reference point that doesn’t change over time
  • Can be created from pre-production data (training/test sets) or production data
  • Consistent comparison point for detecting absolute drift
  • Ideal for compliance, audit requirements, and stable model environments
  • Pre-production static baselines created via model.publish() with PRE_PRODUCTION environment
  • Production static baselines defined using specific time ranges
Rolling Baselines:
  • Dynamic sliding window that shifts with time
  • Always maintains fixed time distance from current data (e.g., 4 weeks ago)
  • Automatically adapts to gradual changes in data patterns
  • Excellent for detecting sudden changes or anomalies in time-sensitive data
  • Requires window_bin_size and offset_delta parameters
Selection Guidelines:
  • Use STATIC for regulatory compliance, model validation, and stable environments
  • Use ROLLING for seasonal patterns, evolving data, and operational monitoring
  • Static pre-production baselines are recommended for most use cases
  • Rolling baselines work best with sufficient historical production data

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Example

# Static production baseline using time range
static_baseline = fdl.Baseline(
    name="static_baseline",
    model_id=model.id,
    environment=fdl.EnvType.PRODUCTION,
    type_=fdl.BaselineType.STATIC,
    start_time=(datetime.now() - timedelta(days=30)).timestamp(),
    end_time=(datetime.now() - timedelta(days=7)).timestamp()
).create()

# Rolling production baseline with monthly window
rolling_baseline = fdl.Baseline(
    name="rolling_baseline",
    model_id=model.id,
    environment=fdl.EnvType.PRODUCTION,
    type_=fdl.BaselineType.ROLLING,
    window_bin_size=fdl.WindowBinSize.MONTH,
    offset_delta=1  # 1 month offset
).create()

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STATIC

Fixed baseline using historical reference data or specific time ranges

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ROLLING

Dynamic sliding window baseline that shifts with time

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STATIC = ‘STATIC’

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ROLLING = ‘ROLLING’