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updatedAt: 2025-06-17T21:18:05.000Z
agentTools:
projectIndex: https://documentation.picsellia.com/llms.txt
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# 9. Deploy a ModelVersion and set the Data Pipeline up
*Objectives:*
* [ ] Deploy your `DatasetVersion`
* [ ] Set your *Data Pipeline* up
* [ ] Access your *Model Monitoring Dashboard*
## 1. Deploy your `ModelVersion`
From the *Model Version view,* you can now deploy your `ModelVersion` to make it able to perform `Prediction` by clicking on *Deploy*.
The *Deployment* can be done on the *OVH infrastructure* provided by Picsellia or directly on your own infrastructure.
While deploying the `ModelVersion`, a *confidence threshold* will be asked, be advised that any `Shape` with a *confidence score* lower than this threshold won't be taken into account, as a consequence we advise you to put a low threshold first and adjust it later in the *Settings* of the `Deployment`.
Once the *confidence threshold* is defined, the `ModelVersion` is deployed. You can access all your `Deployment` from the *Deployment* view and select any of them to access its *Monitoring Dashboard*.
## 2. *Monitoring Dashboard*
The *Monitoring Dashboard* of a `Deployment` view allows you to access many metrics related to your `ModelVersion` performances such as latency, heat map, KS Drift, Outlier score, mAP, and global distribution…. By the way, you can find details about them over there.
Right after the `ModelVersion` *deployment*, those *metrics* are supposed to be empty until `Prediction` are done by our `ModelVersion`.
## 3. Set the *Data Pipeline* up
The main idea of this *Data Pipeline* is to ensure that the `ModelVersion` is always improving by leveraging human-reviewed production `Data`.
As a consequence, before making a `Prediction`, it is useful to set up the following things in the *Settings* tab:
* *The training Data* indicates the `DatasetVersion` used to train the deployed `ModelVersion` so *supervised metrics* such as *KS Drift* can be computed. If you pushed and deployed a `ModelVersion` on Picsellia without the *Training Data*, it's unnecessary to do it, but be advised that *unsupervised metrics* will not be computed.
* *The feedback loop* is a way to leverage `Data` coming from the production by adding them in a new `DatasetVersion` once humanly reviewed and using this enriched `DatasetVersion` for further training. **The philosophy behind that by using`Data` from the ground to retrain frequently our `ModelVersion` we ensure its performances over time and avoid *Data drift***
* *Continuous training* is part of the pipeline that triggers and orchestrates automatic `ModelVersion` retraining once the *Training Data* has been enriched to enough `Data` coming from the ground.
* **Continuous deployment** is part of the pipeline that exports the retrained `ModelVersion` as a new version and potentially deploys it automatically on the impacted `Deployment` on Picsellia.
> 👍 An automatized process
>
> All those steps are crucial to building a customized and automatized *Data Pipeline*. Once setup you'll be able to retrain and redeploy improved `ModelVersion` without any human action (except the prediction review)
To set everything up, you need to access the *Settings* view of your `Deployment` and browse all tabs to define each step of your *Data Pipeline*.
To activate the computing of *unsupervised metrics* and the *Feedback Loop*, go into the related *Settings* tabs.
Select the `DatasetVersion` used to train the `ModelVersion` deployed for the *Training Data tab* and the `DatasetVersion` enriched with production `Data` for further `ModelVersion` retraining in the *Feedback Loop* tab.
> 🚧 Be patient
>
> When setting up the dataset in *Training Data*, the initialization can take several minutes before the computing of *unsupervised metrics* become available.
*Continuous Training* and *Continuous Deployment* can also be initiated from this “Settings” view.
For continuous training, you will be asked to define the retraining `ModelVersion` parameters such as the project, the `ModelVersion` to retrain, the training `DatasetVersion`, and training *hyperparameters*:
It is important to know that this new training and deployment loop is activated when the number of predictions reviewed by a human and added to the dataset reaches a threshold defined in the "Trigger" subpart.
Regarding the continuous deployment, you've three possibilities:
* **Deploy Shadow**: The new `ModelVersion` trained through *Continuous Training* is deployed as a shadow `ModelVersion` of the current `Deployment`. It means that *Champion* and *Shadow* `ModelVersion` will make `Prediction` for further inferences. It is the best way to assess that the new `ModelVersion` is over-performing the previous one before turning it into the *champion*.
* **Deploy Champion**: Replace the *Champion* `ModelVersion` with the new `ModelVersion` created through *Continuous Training*. Further inferences will be done by the newly created `ModelVersion`.
* **Deploy manual**: Do not deploy the new `ModelVersion` created through *Continuous Training*. This new `ModelVersion` remains stored on your *Private Model Registry*.
You can ensure that your whole *Data Pipeline* is well activated from the *Dashboard view*:
> 👍 The "Settings" tab also allows you to update the *confidence threshold* and the name of your `Deployment`