From the Deployments list, by clicking on the Name of a
Deployment, you will land on the Deployment overview.
This overview is composed of three tabs:
- Dashboard, where you can access the metrics related to performances of the
ModelVersionin the frame of the current
- Predictions, which allows you to visualize the predicted images and associated Predictions
In the header of the Deployment overview, you can see the
DeploymentName. This name is randomly generated by Picsellia when instantiating the
Deployment, but for sure it can be renamed in the Settings tab.
The Dashboard tab aims to give you a clear and comprehensive idea of the health status of your
It is composed of several contextual information and metrics.
The contextual information are displayed on top of the Dashboard. You'll retrieve:
- Champion Model: The
ModelVersiondeployed in the current
Deploymentas a clickable link.
- Shadow Model: The
ModelVersionpotentially deployed as a Shadow Model in the current
Deploymentas a clickable link.
- The number of inferences performed already in this
Deploymentand the associated mean latency
- The list of Alerts triggered in the frame of this
Below, the Dashboard displays many types of metrics that any user can use to assess the quality of the
Those metrics are divided into two categories:
- Supervised metrics
- Unspuservised metrics
Details about each metric available are listed in the dedicated page here.
The Predictions tab displays an overview of all the
Prediction logged in the current
The Predictions overview followed the same philosophy as other image overviews on Picsellia such as the Assets overview. This overview will allow you to visualize all the
PredictedAssest, related Metadata and access the Prediction Review tool.
From the Settings tab, you can:
- Edit general information related to the current
- Setup and edit your Pipeline
- Create and edit Alerts
Let's have a look then in the General section of the Settings tab.
From here, you can rename your
Deployment or modify its Confidence Threshold (meaning that any
Shape with a confidence score below this threshold will be ignored), after any modification do not forget to save them by clicking on Update Deployment
From the General tab you can also delete the current
Deployment by clicking on Delete this deployment, for security reasons, you will be asked to prompt the
All the informations (Metrics,
Prediction...) logged in the current
Deploymentwill be deleted.
However, as explained previously, each
PredictedAsset logged in a
Deployment is physically stored through the proper Storage Connector on the storage associated with the target
Datalake selected while deploying the
It means that each time a
PredictedAsset is logged to a
Deployment a new
Data is created in the target
Datalake. As it is the case for
PredictedAsset inherits from
This means that when you delete a
Data, any related
PredictedAsset will be removed from their
Deployment. But, when deleting a whole
Deployment, all the logged
PredictedAsset will be deleted but their related
Data will remain in the target
Updated about 2 months ago