Deploying Models and Workflows

Once a workflow is developed, there are several ways that it can be deployed or operationalized.

Production, deployment, and optimization are explored in the following use cases.

Moving a Workflow from Development to Production
Typically, a workflow is developed within one environment-perhaps a private folder on Hadoop, or a development schema-and then deployed within another. You can easily switch which data sources a workflow executes in, and then to transition completed flows from development to production.
Preparing Data and Deploying Models
In many cases, data science teams team prepare data from a variety of different sources before modeling.
Optimizing Models
Operations teams should simplify and optimize the workflows that are created in the development environment by the analytics team. Over time, the operations team can use the collaborative environment to encourage and enforce best practices, by creating template workflows and custom operators that encapsulate standards and optimizations.