We are no longer updating the Amazon Machine Learning service or accepting new users for it. This documentation is available for existing users, but we are no longer updating it. For more information, see What is Amazon Machine Learning.
Names and IDs for all Objects
Every object in Amazon ML must have an identifier, or ID. The Amazon ML console generates ID values for you, but if you use the API you must generate your own. Each ID must be unique among all Amazon ML objects of the same type in your AWS account. That is, you cannot have two evaluations with the same ID. It is possible to have an evaluation and a datasource with the same ID, although it is not recommended.
We recommend that you use randomly generated identifiers for your objects, prefixed with a short string to identify their type. For example, when the Amazon ML console generates a datasource, it assigns the datasource a random, unique ID like "ds-zScWIuWiOxF". This ID is sufficiently random to avoid collisions for any single user, and it's also compact and readable. The "ds-" prefix is for convenience and clarity, but is not required. If you're not sure what to use for your ID strings, we recommend using hexadecimal UUID values (like 28b1e915-57e5-4e6c-a7bd-6fb4e729cb23), which are readily available in any modern programming environment.
ID strings can contain ASCII letters, numbers, hyphens and underscores, and can be up to 64 characters long. It is possible and perhaps convenient to encode metadata into an ID string. But it is not recommended because once an object has been created, its ID cannot be changed.
Object names provide an easy way for you to associate user-friendly metadata with each object. You can update names after an object has been created. This makes it possible for the object's name to reflect some aspect of your ML workflow. For example, you might initially name an ML model "experiment #3", and then later rename the model "final production model". Names can be any string you want, up to 1,024 characters.
