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

Pre-requisite

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.

Deploy

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 os
import requests
import json

token = "0e1220d4ce70323188f5d3646cfd877caedba23"
deployment_name = "every-envelope"
url = "https://steroids.picsellia.com/api/v1/predict/{}".format(deployment_name)

headers = {
    "Authorization": "Token " + token,
}

data = {
    "source": "test",
    "tag": "test_tag1"
}

imgpath = "pexels-sergio-souza-2293649.jpg"

r = requests.post(
    url=url,
    files={'media': open(imgpath, 'rb')},
    headers=headers,
    data=data
)

prediction = r.json()

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