With Picsellia πŸ₯‘ serverless deployment you can deploy your exported models easily.


In order to benefit from our serverless deployment engine, you need to have some exported models in your model registry.

Select the model that you want to deploy

To select the model, browse into your model registry page.

Let's select version 0.0 of our sample-model model πŸ₯‘.

You can quickly see the overview of your exported model i.e the training set used and the experiment that created this model.


You have 3 different options:

  • Deploy Serverless.
  • Use your Custom Serving.
  • Only use your deployment dashboard to Monitor your models.

Access your deployment dashboard

Your deployment is now created and you can access its name on top-left.

You now have access to your deployment dashboard, this is the one-stop place to:

  • Model Management.
  • Latency Monitoring.
  • Prediction images visualization and review.
  • Alerting, etc.

If you want more information about all the monitoring metrics provided in the Picsellia monitoring, please check the Model Monitoring Section of Picsellia's documentation πŸ₯‘.

Try prediction

import requests
import json

from picsellia import Client

client = Client(
  api_token="YOUR API TOKEN",   # Can be retrieved from your profile on the platform.
    organization_name="YOUR ORGANIZATION NAME" # Default to your organization.

# Get deployment by its ID. 
my_deployment = client.get_deployment_by_id(id="deployment-id")
# Get deployment by name.
#my_deployment = client.get_deployment(name="deployment-name")

# Get your deployment ID.
deployment_id = str(my_deployment.id)

# Get your API token from your profile. 
api_token = "YOUR API TOKEN"

# Authentication url 
auth_url = "https://serving.picsellia.com/api/login"

headers = {
    "Authorization": "Token " + api_token,

jwt_generation_data = {
    "deployment_id": deployment_id,
    "api_token": api_token

jwt_request = requests.post(

# Retrieving the JWT. 
jwt = jwt_request.json()["jwt"]

# Serving API endpoint 
url = "https://serving.picsellia.com/api/deployment/{}/predict".format(my_deployment.id)

header = {
    "Authorization": "Bearer " + jwt,

# Metadata that helps extrat data from datalake.
data = {
    "source": "camera1",   
    "tag": "tag"     
imgpath = "path/to/image"

with open(imgpath, "rb") as file:
    r = requests.post(
        files={'media': file},

prediction = r.json()

Now you have a scalable and serverless API endpoint that won't change even if you change your model version :).