Publishes a batch of events to Fiddler asynchronously using a schema for locating fields within complex data structures.

Input ParameterTypeDefaultDescription
batch_sourceUnion[pd.Dataframe, str]NoneEither a pandas DataFrame containing a batch of events, or the path to a file containing a batch of events. Supported file types are

- CSV (.csv)
publish_schemadictNoneA dictionary used for locating fields within complex or nested data structures.
data_sourceOptional [fdl.BatchPublishType]NoneThe location of the data source provided. By default, Fiddler will try to infer the value. Can be one of

- fdl.BatchPublishType.DATAFRAME
- fdl.BatchPublishType.LOCAL_DISK
- fdl.BatchPublishType.AWS_S3
credentialsOptional [dict]NoneA dictionary containing authorization information for AWS or GCP.

For AWS, the expected keys are

- 'aws_access_key_id'
- 'aws_secret_access_key'
- 'aws_session_token'For GCP, the expected keys are

- 'gcs_access_key_id'
- 'gcs_secret_access_key'
- 'gcs_session_token'
group_byOptional [str]NoneThe field used to group events together when computing performance metrics (for ranking models only).
PROJECT_ID = 'example_project'
MODEL_ID = 'example_model'

path_to_batch = 'events_batch.avro'

schema = {
    '__static': {
        '__project': PROJECT_ID,
        '__model': MODEL_ID
    '__dynamic': {
        'feature_1': 'features/feature_1',
        'feature_2': 'features/feature_2',
        'feature_3': 'features/feature_3',
        'output_column': 'outputs/output_column',
        'target_column': 'targets/target_column'
      ORG = '__org'
13      MODEL = '__model'
14      PROJECT = '__project'
15      TIMESTAMP = '__timestamp'
16      DEFAULT_TIMESTAMP = '__default_timestamp'
17      TIMESTAMP_FORMAT = '__timestamp_format'
18      EVENT_ID = '__event_id'
19      IS_UPDATE_EVENT = '__is_update_event'
20      STATUS = '__status'
21      LATENCY = '__latency'
22      ITERATOR_KEY = '__iterator_key'