With Picsellia 🥑 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.
To select the model, browse into your
model registry page.
version 0.0 of our
sample-model model 🥑.
You can quickly see the overview of your exported model i.e the training set used and the experiment that created this model.
You can deploy on our Picsellia Serving !
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 🥑.
from picsellia import Client from picsellia.types.schemas_prediction import DetectionPredictionFormat api_token = "<YOUR_API_TOKEN>" organization_name = "<ORGANIZATION_NAME>" deployment_name = "<DEPLOYMENT_NAME>" # You can also use your organization_id, please be sure it's an UUID if you're in a version < 6.6 client = Client(api_token=api_token, organization_name=organization_name) deployment = client.get_deployment(deployment_name) data = deployment.predict(file_path="/path/to/image.png") print(data)
Now you have a scalable and serverless API endpoint that won't change even if you change your model version :).
Updated 5 months ago