Skip to main content

Documentation Index

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

Use this file to discover all available pages before exploring further.

Fiddler allows you to update specific fields in previously published events. While your model feature values can’t be updated, you can update:
  • Target column values (ground truth labels)
  • Metadata columns
Input, Output, and Custom Feature column types cannot be updated once published. If values for these columns are included with your update (update_event=True), they will be ignored.

​
Updating Ground Truth Labels

Updating ground truth labels is the most common use case for post-publish updates. You can update events when:
  • Actual values become available for events initially published without labels
  • You discover that initially uploaded labels are incorrect
Things to keep in mind about ground truth labels (target values):
  • Use null values when initially publishing inferences that don’t yet have labels
  • Fiddler automatically keeps aggregated performance metrics current as labels are updated
  • Labels can be updated multiple times if necessary

​
Updating Metadata Columns

Fiddler supports updating metadata columns with new values. This is particularly useful for supporting alternate labels that can be used with Custom Metrics to calculate alternative performance metrics. Things to keep in mind regarding metadata updates:
  • Updated values are visible in Feature Analytics and Root Cause Analysis views
  • Pre-calculated aggregated metrics will not reflect the updated values
  • Custom Metrics, used in charts and alerts, will always use the current values since they’re calculated at runtime
  • Updating metadata columns requires additional processing time, so only send updates when necessary

​
Label Update Examples

​
Stream Label Updates

As with inference publishing, label updates can be sent as streams or batches. Stream Update Data Formats
  • List of Python dictionaries
Stream Update One or More Events Integrate Fiddler directly into your production system to publish label updates as they occur. The event ID, the column chosen for Model.event_id_col, is required. 
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

event_update = [
    {
        'label_value': 1,
        'event_id': 'A1',
    },
    ...
]

# Streaming update returns the list of event ID(s) updated.
event_id = model.publish(
    source=event_update,
    environment=fdl.EnvType.PRODUCTION,
    update=True,
)
Stream Update Label and Metadata Metadata column(s) can also be updated. 
import fiddler as fdl

# Instantiate the Model object for your model
project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)

event_update = [
    {
        'metadata_col': 'yes',
        'label_value': 1,
        'event_id': 'A1',
    },
]

event_id = model.publish(
    source=event_update,
    environment=fdl.EnvType.PRODUCTION,
    update=True,
)

​
Batch Update Data Formats

  • pandas DataFrame
  • CSV file (.csv),
  • Parquet file (.parquet)
Batch Update Events Publish larger sets of label updates using batch publishing. The event ID, the column chosen for Model.event_id_col, is required. 
import pandas as pd
import fiddler as fdl

project = fdl.Project.from_name(name='your_project_name')
model = fdl.Model.from_name(name='your_model_name', project_id=project.id)
label_batch = pd.read_parquet('path_to_your_updated_labels/data.parquet')

update_job = model.publish(
    source=label_batch,
    environment=fdl.EnvType.PRODUCTION,
    update=True,
)
Refer to Model.publish() documentation for more details on different sources and parameters.
📘 There are a few points to be aware of:
  • Performance metrics (available in monitoring charts and alert rules) will be computed as ground truth labels are inserted and recomputed when later updated.
    • For example, if the ground truth values are originally missing from events in a given time range, there will be no performance metrics available for that time range. Once the events are updated, performance metrics will be computed and will populate the monitoring charts.
    • Events that do not originally have ground truth labels should be uploaded with empty values—not dummy values. If dummy values are used, you will have improper performance metrics, and once the new values come in, the old, incorrect values will still be present.
    • Metrics based on Metadata columns won’t reflect updates.
  • In order to update existing events, you will need access to the event IDs used at the time of upload.
  • Updating the event timestamp, Model.event_ts_col, is not supported.