Model Manager allows you to store, manage and generally govern all of your models in a single, central location. Emphasis on ‘all’: you do not have to develop your models in SAS to register them in Model Manager. Notably, this means that Model Manager is compatible with models created using open source tools such as R and Python. PMML (predictive modelling mark-up language) is also supported. The product includes model versioning and version control, as well as history tracking. Models can be grouped into projects, and both individual models and projects can be shared between users or team members to enable collaborative working.
Model Manager is accessible via a web browser, through which you can register new models and manage existing ones. ‘Manage’, in this case, includes monitoring (to ensure that models are performing as well as you would like), reporting, deployment, and retraining, among other things. To support these functions, a dashboard is provided to display a variety of analytics and metrics relating to your models, and how they are performing.
On the subject of (re)training, Model Studio, a secondary product available from SAS, allows you to build pipelines for training your models. When you send a model to be retrained, it is reinserted into the appropriate pipeline with new training data attached (alternatively, you can simply flag poorly performing models for a data scientist to look at, and leave it at that). As alluded to above, this can be done at will and ad-hoc.
Model Manager is also designed to promote model reuse. This is the idea that any given model, or variants thereof, should only be developed once, but can (and often should) be deployed multiple times to multiple locations. Accordingly, models in Model Manager can a) be deployed as many times as you want and b) can be deployed to a variety of targets, including databases, data lakes, and streams (including SAS Event Stream Processing), as well as within the SAS platform itself. Moreover, models can be deployed to multiple targets at once with a single click.

Fig 02 - Decision flows and workflows in SAS Model Manager
Having stored and governed your models within Model Manager, SAS provides two products for integrating those models within your business processes. The first of these is SAS Decision Manager, a framework for creating deployable decision flows that incorporate analytics models, policy rules and business logic. The second is SAS Workflow Manager, which allows you to create bespoke, automated workflows that include analytics models. Both decision flows and workflows, which are illustrated in Figure 2, are created using drag and drop interfaces within their respective products, and the latter in particular includes notifications and tasks to prompt expedient workflow completion.
SAS also provides functionality for creating and managing tests for your models, as well as model output validation and scoring. In particular, this includes ‘publishing validation’: testing (and therefore validating) your model as it runs on a particular environment with a particular data source. In other words, you can test in the exact conditions your model is going to be running in. This has obvious advantages.
Finally, SAS supplies a variety of visual analytics which are applicable to models and help with explainability. Most notably, this includes a decision tree diagram, as well a ‘root cause’ analysis of your model’s structure.