[FR] trigger deployment of models via MLFLOW UI

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Willingness to contribute

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• Yes. I would be willing to contribute this feature with guidance from the MLflow community.
• No. I cannot contribute this feature at this time.

Proposal Summary

In order to have Mlflow as a central component in the model deployment and governance process, we would like to trigger model deployments by also using the MLFLOW UI and using the mlflow model stage button .

Right now this is not available and the work around is to have a small daemon running all the time, pulling information from the model registry and deploy or undeploy models accordingly . If we could have an even triggered from MLFLOW server when a model state transition is happening then we could save a lot of time .

I could be willing to investigate how much effort this is and potentially implement this. What I would like is some direction on where in the code to look for propagating those events from the GUI to the backend server.

Motivation

• What is the use case for this feature?
• Why is this use case valuable to support for MLflow users in general?
• Why is this use case valuable to support for your project(s) or organization?
• Why is it currently difficult to achieve this use case? (please be as specific as possible about why related MLflow features and components are insufficient)

What component(s), interfaces, languages, and integrations does this feature affect?

Components

• area/artifacts: Artifact stores and artifact logging
• area/build: Build and test infrastructure for MLflow
• [ x] area/docs: MLflow documentation pages
• area/examples: Example code
• area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
• area/models: MLmodel format, model serialization/deserialization, flavors
• area/projects: MLproject format, project running backends
• area/scoring: Local serving, model deployment tools, spark UDFs
• [x ] area/server-infra: MLflow server, JavaScript dev server
• area/tracking: Tracking Service, tracking client APIs, autologging

Interfaces

• [x ] area/uiux: Front-end, user experience, JavaScript, plotting
• area/docker: Docker use across MLflow’s components, such as MLflow Projects and MLflow Models
• area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
• area/windows: Windows support

Languages

• language/r: R APIs and clients
• language/java: Java APIs and clients
• language/new: Proposals for new client languages

Integrations

• integrations/azure: Azure and Azure ML integrations
• integrations/sagemaker: SageMaker integrations
• integrations/databricks: Databricks integrations

Details

(Use this section to include any additional information about the feature. If you have a proposal for how to implement this feature, please include it here. For implementation guidelines, please refer to the Contributing Guide.)

[hugowschneider]

Hi @echarso and @dmatrix,

I was looking into this FR and I am very interested in following up on that and if possible contribute 😃, have any of you started any implementation?

I like the proposed approach to implemented is similar to the plugins. I was hoping to achieve with this a 2-step approach.

1. A callable would give feedback, based on the information stored in MLFlow, if a model can be moved from one stage to another. This would be an automated feedback besides the manual one.
2. After approving this “move” a second callable would trigger the model deploy.

Does this make sense?

[alexott]

@dmatrix I have this idea in my head for a some time already, I just need to find time to prepare proposal - I maybe need several days for that, hopefully over this weekend.