SAS Viya and Python Integration for Machine Learning

There is a new version of this course. Please see SAS Viya and Python Integration for Machine Learning.

In this course, you learn to use the Python API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar Python functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You then learn to download results to the client and use native Python syntax to compare models.

Learn how to

• Use the Python API in SAS Viya.
• Submit CAS actions from Python.
• Manage, alter, and prepare data on the CAS server.
• Implement and compare machine learning models on the CAS server.
• Move data between the client and server.
• Use Python syntax to wrap up CAS actions with functions and loops.
• Promote data to persist in memory.

Who should attend

Python users who want to take advantage of distributed analytics in SAS Viya for fast and efficient modeling

Formats available	Standard Duration (duration can vary, see event schedule for details)
Classroom:	1 day
Live Web Classroom:	2 half-day session(s)	System Requirements

Before taking this class, you should have experience writing Python programs for data analytics. There are no SAS prerequisites.

This course addresses SAS Viya, SAS Visual Data Mining and Machine Learning software.

SAS Viya and Open Source Integration

• SAS Viya and SAS Cloud Analytic Services (CAS).
• Open-source development interfaces.
• Scripting Wrapper for Analytics Transfer (SWAT).
• Fundamentals of the R and Python APIs.

Machine Learning

• Predictive modeling.
• Predictive models.
• Model assessment.