Export Data from Arize to Phoenix

Easily share data when you discover interesting insights so your data science team can perform further investigation or kickoff retraining workflows.

Oftentimes, the team that notices an issue in their model, for example a prompt/response LLM model, may not be the same team that continues the investigations or kicks off retraining workflows.

With a few lines of Python code, users can export this data into Phoenix for further analysis. This allows team members, such as data scientists, who may not have access to production data today, an easy way to access relevant product data for further analysis in an environment they are familiar with.

They can then easily augment and fine tune the data and verify improved performance, before deploying back to production.


from datetime import datetime

from arize.exporter import ArizeExportClient
from arize.utils.types import Environments

client = ArizeExportClient()

primary_df = client.export_model_to_df(

Test out this workflow by signing up for a free Arize account.

Last updated