Deploying assets

For proper deployment, you must set up a deployment space and then select and configure a specific deployment type.

Refer to these topics:

• Deployment spaces. You can use spaces to deploy models and manage your deployments.
• Creating an online deployment. Create an online (also called Web service) deployment to load a model or Python code when the deployment is created, to generate predictions online, in real time.
• 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 file.
• Deploying Python functions. Deploying functions gives you the ability to hide details (such as credentials), preprocess data before passing it to models, perform error handling, and include calls to multiple models, all within the deployed function instead of in your application.
• Creating a Shiny app deployment. Save a Shiny app to a deployment space, then deploy it as an app and make the URL available to users.
• Deploying scripts. Deploy Python or R scripts
• Creating a deployment job. From a deployment space, you can create, schedule, run, and manage jobs that process data for batch deployments, Python functions, and scripts.
• Getting the deployment endpoint URL. To send payload data to a model or function deployment for analysis, you need to know the endpoint URL of the deployment.

Parent topic: Deploying and managing models and functions