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ModelOps - Basics

Started ‎02-05-2021 by
Modified ‎05-18-2021 by
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Interested in ModelOps with SAS Model Manager and not sure where to get started? Please take a look at this is short video explaining how to register a Python model into SAS Model Manager and deploying the Python model for in-memory batch scoring using SAS.

 

SAS Model Manager provides support for the analytical model lifecycle for SAS and Open Source models.  The software provides capabilities for common model storage, metadata, search, selection, testing, deployment, monitoring, KPI-based alerting, and dashboards. These functions enable easy model access and integration by data scientists, business users, and information technology (IT) teams.

 

There is more to a model than the pickle file you created. To run your model in production, you need to know:

  • what input variables are needed by your model
    • A good best practice is providing acceptable range of values for each input variable, based on your testing data.
  • what output variables the model generates
    • A good best practice is providing what "good looks like" and the expected distribution by output variables, based on your testing data. For instance, expected probability of 1s is 90% and 0s is 10%.

 

SAS Model Manager's Model Repository API registers all model assets. To facilitate use of the API, SAS created the Python Zip Model Management (PZZM) package, available on the sassoftware organization on GitHub.

 

From this central, governed hub for all model sources and types, the model is published to a CAS destination. CAS is one of multiple deployment options. Other destinations include SAS' real-time scoring Micro Analytics Service (MAS) and containers. More details are available in the documentation.

 

The video below demonstrates how to apply the model to new data using theSWAT package. The SWAT package allows Python users to make a connection to CAS and run CAS actions.

 

 

The three steps outlined here lay the foundation of a solid, maintainable and enterprise ready ModelOps environment. Please note, creating an enterprise ready system for ModelOps requires more details. Hans-Joachim Edert and I discussed several aspects during of the process in the following meetup video:

 

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Last update:
‎05-18-2021 09:50 AM
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