The MLflow Community encourages new feature contributions. Would you or another member of your organization be willing to contribute an implementation of this feature (either as an MLflow Plugin or an enhancement to the MLflow code base)?
[x] Yes. I can contribute this feature independently.
[x] 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
Hello everyone,
I would like to be able to add the possibility to add user rights to mlflow tracking, in order to limit the view of experiments only to people who have the right to do so, all this using a single tracking server, so a user to connect to the tracking server will provide a token.
I'm a new mlflow user so I'd like to have some hints to start and if you think it's feasible (with a plugin or by modifying the base source code),
Thanks in advance for your answers !
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
[ ] 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
[ ] area/server-infra: MLflow server, JavaScript dev server
Willingness to contribute
The MLflow Community encourages new feature contributions. Would you or another member of your organization be willing to contribute an implementation of this feature (either as an MLflow Plugin or an enhancement to the MLflow code base)?
Proposal Summary
Hello everyone,
I would like to be able to add the possibility to add user rights to mlflow tracking, in order to limit the view of experiments only to people who have the right to do so, all this using a single tracking server, so a user to connect to the tracking server will provide a token.
I'm a new mlflow user so I'd like to have some hints to start and if you think it's feasible (with a plugin or by modifying the base source code),
Thanks in advance for your answers !
What component(s), interfaces, languages, and integrations does this feature affect?
Components
area/artifacts
: Artifact stores and artifact loggingarea/build
: Build and test infrastructure for MLflowarea/docs
: MLflow documentation pagesarea/examples
: Example codearea/model-registry
: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models
: MLmodel format, model serialization/deserialization, flavorsarea/projects
: MLproject format, project running backendsarea/scoring
: Local serving, model deployment tools, spark UDFsarea/server-infra
: MLflow server, JavaScript dev serverarea/tracking
: Tracking Service, tracking client APIs, autologgingInterfaces
area/uiux
: Front-end, user experience, JavaScript, plottingarea/docker
: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy
: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows
: Windows support