First API (with API Deployer)¶
This page will guide you through the process of creating and deploying your first API service. For this example, we’ll use a prediction endpoint, used to expose a model developed using the DSS visual machine learning component as a REST API service.
The steps to expose a prediction model are:
Warning
This section assumes that you already have installed and configured the DSS API Deployer, and already have an infrastructure connected to it. Please see Setting up the API Deployer and deployment infrastructures if that’s not yet the case.
Create the model¶
The first step is to create a model and deploy it to the Flow. This is done using the regular Machine Learning component of DSS. Please refer to the Machine Learning Basics and to Machine learning for more information.
Create the API Service¶
There are two ways you can create your API Service:
Create the API directly from the Flow¶
Note
This method can only be used for prediction endpoints, and cannot be used for other kinds of endpoints
In the Flow, select your model, and click “Create an API”
Give an identifier to your API Service. This identifier will appear in the URL used to query the API
Within this API Service, give an identifier to the endpoint. A service can contain multiple endpoints (to manage several models at once, or perform different functions)
The URL to query the API will be like /public/api/v1/<service_id>/<endpoint_id>/predict for prediction models, and /public/api/v1/<service_id>/<endpoint_id>/forecast for time series forecasting models.
Click Append, and you are taken to the newly created API Service in the API Designer component.
Create the API service then the endpoint in API Designer¶
Go to the project homepage
Go to the API Designer and create a new service
Give an identifier to your API Service. This identifier will appear in the URL used to query the API
At this point, the API Service is created but not yet have any endpoint, i.e. it does not yet expose any model. See Concepts for what endpoints are.
Create a new endpoint of type “Prediction”. Give an identifier to the endpoint. A service can contain multiple endpoints (to manage several models at once, or perform different functions)
Select the model to use for this endpoint. This must be a saved model (ie. a model which has been deployed to the Flow).
The URL to query the API will be like /public/api/v1/<service_id>/<endpoint_id>/predict for prediction models, and /public/api/v1/<service_id>/<endpoint_id>/forecast for time series forecasting models.
Click Append, and you are taken to the newly created API Service in the API Designer component.
For a simple service, that’s it! You don’t need any further configuration.
(Optional) Add test queries¶
It’s a good practice to add a few test queries to check that your endpoint is working as expected, both in the API Designer and the API Deployer
Go to test queries
Select add test queries. You can select a “test” dataset to automatically create test queries from the rows of this dataset
Click on “Run test queries”
You should see the prediction associated to each test query
Push a version to the API Deployer¶
Click on “Push to API Deployer”. This does two things:
It creates the first Version (i.e. snapshot) of your API service using the currently active version of the saved model.
It pushes this version to the API Deployer, where it will create a new Published Service on the API Deployer.
Click on the link that appears, which takes you to the API Deployer screen.
Deploy your version¶
In the API Deployer, you now need to actually deploy your service to your infrastructure.
From the left column of the API Deployer, click on the version we just uploaded, and select “Deploy”
Select the infrastructure you wish to deploy to
Give an identifier to your deployment. This identifier will not appear in the URL
Validate
Your deployment is ready. You can either modify its settings, or Start it.
When you click on the “Start” (or the “Update”) button, DSS sends your API Service to the API nodes and activates it. When this process completes, you can see:
The Public URLs from which your applications can query your service
If enabled, monitoring charts for your service
Sample code in various languages that show you how to query your service from various languages
Test queries to check the behavior of your service in the live environment
That’s it, you’ve now deployed your predictive model as an API!
Perform real queries¶
Once you have confirmed that your service endpoint works, you can actually use the API to integrate in your application.
The API Deployer provides prebuilt code samples that you can directly use to query your API nodes.
Next steps¶
Head over to the documentation page for each endpoint to get more information about how to use each one of them.