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 provides multiple options for publishing batches of production data, allowing you to choose the format and method that best suits your needs.
- pandas DataFrame
- CSV file (
.csv),
- Parquet file (
.parquet)
Supported Data Locations
- In memory - pandas DataFrame
- Local disk - CSV, parquet
Note:
Fiddler’s Python client offers the ability to integrate with Cloud data stores such as AWS S3. Refer to our Integrations Guides for examples.
Batch Publishing Examples
Publish a batch of inference events using a parquet file, CSV file, or DataFrame using the Model.publish() function. When publishing in batch mode, Model.publish() executes asynchronously and returns a Job object. The job can be used to:
- Track by ID in the UI on the Jobs page
- Poll for status until completion
- Use the
wait() method for synchronous behavior
- Log the job ID for reference
Parquet File
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)
publish_job = model.publish('my_events_batch.parquet')
# The publish() method is asynchronous. Use the publish job's wait() method
# if synchronous behavior is desired.
publish_job.wait()
CSV File
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)
publish_job = model.publish('my_events_batch.csv')
Pandas DataFrame
import pandas as pd
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)
my_events_df = pd.read_csv('my_events_batch.csv')
publish_job = model.publish(my_events_df)
