Deploy an API endpoint#

See a screencast covering this section’s steps

Although you have created a version of an API service including the endpoint, it exists only on the Design node, which is a development environment. A production use case requires separate environments for development and production. For example:

  • A batch deployment use case would require enabling an Automation node.

  • A real-time API deployment use case (as shown here) could use an API node if staying within Dataiku’s ecosystem. Additionally, depending on your MLOps strategy, you also have external deployment options such as Amazon SageMaker, Azure ML, Google Vertex AI, or Snowflake.

At a high-level, you can think of the entire process in three steps:

  1. Create the API service on the Design node (already done!).

  2. Publish the API service on the Design node to the API Deployer.

  3. Deploy the API service on the API Deployer to a production environment (normally an API node).

Note

Many organizations incorporate an additional governance framework throughout this process. They utilize a Govern node to manage the deployment of projects and models with a built-in sign-off process. Learn more in the Academy course on Dataiku Govern.

Configure an API node#

Before deploying, you first need to configure a production environment. In this example, we’ll use an API node.

From the Design node to the Deployer#

Once you have the necessary infrastructure in place, it’s a few more clicks to actually deploy the endpoint.

  1. From the job_postings API service on the Design node, click Publish on Deployer.

  2. Click Publish, accepting the default version ID.

Dataiku screenshot of the dialog for publishing an API service.

From the Deployer to an API node#

You now have pushed the API service from the Design node to the API Deployer, so let’s navigate there.

  1. Immediately after publishing, you can click the popup notification to Open API Deployer.

  2. If you miss it, open the waffle menu in the top right.

  3. Choose Local Deployer.

  4. Then click Deploying API Services.

Dataiku screenshot of the path to find the local deployer.

Now that you have published the API service to the API Deployer, there is one more step to deploy the endpoint to an API node.

  1. On the API Deployer, find your API service.

  2. Click Deploy.

  3. If not already chosen for you, select an available infrastructure.

  4. Click Deploy again.

  5. Click Deploy once more to confirm.

Dataiku screenshot of the dialog for deploying an API service.

You now have an API endpoint running in a production environment!

Dataiku screenshot of an active API deployment.

Send test queries to the API node#

Once again, let’s test the endpoint with a few more queries — this time sending them to an API node.

  1. From the Status tab of the predict_fake_job endpoint on the API Deployer, navigate to the Run and test subtab.

  2. Click Run All.

Dataiku screenshot of test queries run on the API node.

See also

Once you’ve deployed an API service, the next step would be to monitor it using an Evaluate recipe and a model evaluation store. You’ll learn about these tools as your progress further with Dataiku!