Creating deployments

Creating deployments

A batch deployment processes input data from a file, data connection, or connected data in a storage bucket, and writes the output to a selected destination.

Before you begin

  1. Save a model to a deployment space.
  2. Promote or add the input file for the batch deployment to the space. For details on promoting an asset to a space, refer to Deployment spaces.

Note:

  • These types of batch deployments accept inline payload only:
    • Models based on Spark and PMML frameworks
    • Python functions
  • Batch deployments of Python functions and models based on the PMML framework can only be done programmatically.

Creating a batch deployment

  1. From the deployment space, click the name of the saved model that you want to deploy. The model detail page opens.
  2. Click New deployment.
  3. Choose Batch as the deployment type and enter a name and description for your deployment.
  4. Select hardware specification. For details, refer to Compute requirements for batch deployment jobs.
  5. Click Create. When status changes to Deployed, deployment creation is complete.

Note: Additionally, you can create a batch deployment by using any of these interfaces:

  • Watson Studio user interface, from an Analytics deployment space
  • Watson Machine Learning Python Client
  • Watson Machine Learning REST APIs

Creating batch deployments programmatically

Refer to Machine learning samples and examples for links to sample notebooks that demonstrate creating batch deployments that use the Watson Machine Learning REST API and Watson Machine Learning Python client library.

Viewing deployment details

Click the name of a deployment to view the details.

View deployment details

You can view the configuration details such as hardware and software specifications. You can also get the deployment ID, which you can use in API calls from an endpoint. For details, refer to Looking up a deployment endpoint.

Learn more

Parent topic: Deploying assets