An ML Package is a folder with all the code and metadata needed to train and serve a machine learning model. An ML Package can have multiple versions and is in some way analogous to a package in Orchestrator. Each version can have an associated change log. It is recommended that users acting as Data Scientists handle packages. More details about recommended user personas here.
In order to be used within your workflows in Studio, you first have to deploy them as skills in your tenant.
The ML Packages page, accessible from the ML Packages menu after selecting a project, enables you to view all the available versions of a package, along with their statuses, change logs, and pipelines. Here you can upload new packages or new versions for existing ones, delete undeployed packages, view available information about them, or manage their pipelines.
Click a package in the list to navigate to its corresponding ML Package Details page.