Creating a batch deployment

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.

For information on data sources that are used for scoring batch deployments, refer to Data sources for scoring batch deployments.

For information on required input for scoring batch deployments, depending on model type, refer to Batch deployment input details by framework

For information on how to create a batch deployment job, refer to Creating jobs in deployment spaces.

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.

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:

Creating batch deployments programmatically

Refer to Machine learning samples and examples for links to sample notebooks that demonstrate creating batch deployments using 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.

Parent topic: Deploying assets