---
updatedAt: 2025-06-17T21:18:06.000Z
agentTools:
projectIndex: https://documentation.picsellia.com/llms.txt
---
# 10. Make predictions
*Objectives:*
* [ ] Let your `ModelVersion` make its first `Prediction`
* [ ] Review `Prediction`
* [ ] Access the metrics computed
* [ ] Send reviewed `Prediction` to the data pipeline
* [ ] Observe Continuous training & Continuous deployment
* [ ] Assess that the freshly trained model is over-performing the initial one
## 1. Make `Prediction` with your deployed `ModelVersion`
### A. If your model is served on Picsellia's infrastructure
Now that our deployment is fully set up, we can perform inference on the model to get predictions.
This part can only be done using the Python SDK, so we strongly recommend you rely on the associated documentation available [here](https://documentation.picsellia.com/reference/client).
### B. If you want to serve your model with your own infra
If you want your model to be served by your own infrastructure, you can follow this recipe to store the results of the inferences done by your model and leverage the "Monitoring Dashboard" remotely.
## 2. Review predictions
Once the prediction is done, the monitoring dashboard of the deployment should be updated. The metrics are updated in real time according to the predictions made by the model.
> 🚧 Unsupervised metrics are computer only if "training data" has been activated
The “Predictions” view allows you to visualize and browse among predictions done until now by the model. Once again, you can leverage the search bar to filter among predictions. For instance, you can retrieve inferences that have been human-reviewed, all the inferences with more than X bounding boxes on them, or predictions that have no "car" labels on them.
For each `Prediction`, you have got the possibility to open the *Prediction Review tool* by clicking on *Review*. After landing on the tool, you can review the `Prediction` done by the `ModelVersion` (i.e accept it or modify it).
The *Prediction Review tool* looks like the *Annotation tool*, it allows you to filter the *confidence rate* and modify/add/delete `Predictions`. Once the `Prediction` has been humanly reviewed, you can click on *Review*.
> 👍 Identify high quality images to enrich your `DatasetVersion`
>
> `Predictions` can be sorted by *outlier score*, it allows you know which `Prediction` are the most relevant to review and add in the training set (through *Feedback Loop*) in order to prevent *data drift*.
Once reviewed, the status of the predictions is *Reviewed*, which means that the `Prediction` has been approved/corrected by a human, tho the *review* is used to compute *supervised metrics* on the *Monitoring Dashboard* and it also means that the `Prediction` is ready to be added in the Feedback Loop (i.e `DatasetVersion`defined in the *Settings* tab).
To do it, back to the *Predictions* tab, now each `Prediction` in *Reviewed* status can be selected, then the selection of *Reviewed* `Prediction` can be submitted to the *Feedback Loop* by clicking of *Submit to feedback loop* button ([here](https://documentation.picsellia.com/docs/deploy-a-model-and-set-the-data-pipeline-up) is the link to related documentation).
As soon as done, the `Prediction` goes into *Submitted* status, and the reviewed `Prediction` is added to the selected `DatasetVersion`.
> 📘 *Table and Details views* are also available in the *Predictions overview* allowing you to access `Prediction` *metadata* for instance.
When the number of images submitted to the *Feedback Loop* reaches the threshold defined the in the *Continuous Training*part of the* settings*, a new `Experiment` is automatically launched on the related `Project` with the predefined parameters. By the way, training automatically launched via *Continuous Training* can be recognized with the timestamp at the end of the `Experiment` name.
Once the `Experiment` is over, it will be exported as a new version of the currently deployed `ModelVersion` and deployed according to the *Continuous Deployment* policy.
In the case of *Shadow Deployment*, for further predictions, you can switch between *Champion model prediction*and* Shadow model prediction* in the top right corner of the *Prediction Review tool*: