BaselineType

• 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

• 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

• 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

​

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()

​

STATIC

​

ROLLING

​

STATIC = ‘STATIC’

​

ROLLING = ‘ROLLING’