[FR] Compatibility with MLflow 2.0

[dbczumar]

Proposal Summary

In MLflow 2.0 (scheduled for release on Nov. 14), we will be making small modifications to the MLflow Model Server's RESTful scoring protocol (documented here: https://output.circle-artifacts.com/output/job/bb07270e-1101-421c-901c-01e72bc7b6df/artifacts/0/docs/build/html/models.html#deploy-mlflow-models) and the MLflow Deployment Client predict() API (documented here: https://output.circle-artifacts.com/output/job/bb07270e-1101-421c-901c-01e72bc7b6df/artifacts/0/docs/build/html/python_api/mlflow.deployments.html#mlflow.deployments.BaseDeploymentClient.predict).

For compatibility with MLflow 2.0, the mlflow-redisai plugin will need to be updated to conform to the new scoring protocol and Deployment Client interface. The MLflow maintainers are happy to assist with this process, and we apologize for the short notice.

Motivation

• What is the use case for this feature? Provide a richer, more extensible scoring protocol and broaden the deployment client prediction interface beyond dataframe inputs.
• Why is this use case valuable to support for MLflow RedisAI Deployment plugin users in general? Necessary for compatibility for MLflow 2.0
• Why is it currently difficult to achieve this use case? Without these changes, the mlflow-redisai plugin will break in MLflow 2.0.

[dbczumar]

@hhsecond @gkorland @chayim Do you have bandwidth on your end to migrate the mlflow-redisai plugin to the updated scoring protocol and adjust the Deployment Client predict() API? By my estimates, it should only take a few hours of work at most. Apologies for the short notice.

[alonre24]

Hi @dbczumar,
Thank you for notifying us of the breaking change, and apologies for the delayed response. We will look into that and see if we can perform the required changes to support MLflow 2.0.