Models
Models Management Overview
Last updated
Models Management Overview
Last updated
Data scientists requires to repeat training models with various combinations of dataset, feature, parameters etc., and conducting experiments on models, furthermore, to register/to version models which have decent performance according to results. Nowadays, this is one part of MLOps.
Regarding managing versioned models, PrimeHub, by integrating well-known MLflow, provides models management feature, Models that scientists can examine the performance of versioned/registered models and deploy a selected model directly as a service by Deployments on PrimeHub.
A running installed MLflow instance is required and Group Setting has to be configured with relative information.
MLflow setting is not configured yet
Mlflow instance is not reachable/running
The page displays registered models from binding MLflow.
If a loading page displays only, please double check MLflow Tracking URI configuration of MLflow setting in Group Setting.
MLflow UI button: navigate to binding MLflow server in a new tab.
As long as an experimental model is registered on MLflow, it is listed in Models on PrimeHub as well.
By clicking each model name, it navigates into the list of versioned models.
Version: Version number
Registered At: The registration date/time
Deployed By: The deployment name if the model is used for a deployment; click to navigate into the deployment detail page.
Parameters: selected parameters of the model
Metrics: selected metrics of the model
Deploy button: Deploying the selected versioned model.
Parameters and Metrics
Clicking Columns and select parameters and metrics to display as columns in the table.
The page displays the information regarding this version.
Registered At
Last Modified
Source Run: linking to the run on MLflow
Parameters: if any
Metrics: if any
Tags: if any
In order to deploy a certain versioned model, click Deploy of a versioned model and select + Create new deployment or update an existing deployment. It will navigate to Deployment page, continue to submit the deployment with mandatory information.
The model which is used for the deployment is with the information of the deployment name under Deployed by column. Click the deployment will navigate into the deployment detail page.
From the deployment information page, Model URI presents models:/<model_name>/<model_version>, e.g., models:/tensorflow-model/2.
models:/: the model which is tracked by MLflow is deployed from Model Management
<model_name>:the name of the model
<model_version: the version number of the model
