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Machine Learning using SAS code, Python and Visual Analytics -- all in one place! Viya!

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SAS Employee
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Machine Learning using SAS code, Python and Visual Analytics -- all in one place! Viya!

Good afternoon everyone. I’m very excited to share details about our updated version of SAS Visual Data Mining and Machine Learning on SAS Viya.

 

Before I dive into the details, it’s important to understand the thinking behind this revolutionary release. As the need for machine learning as skyrocketed, so has the need to access methods from multiple entry points. Organizations are often made up of a diverse set of individuals with varying backgrounds in computer science, statistics, machine learning, and business. Accompanying these backgrounds is the myriad of analytical ‘tools’ that you need to solve modern business problems. Examples may include SAS, which includes the SAS language and our graphical users interfaces.  You may have a background in Python, R, Java or Lua. You may even be an application developer who wants to build applications from the ground-up using APIs. No matter your skillset, you should be able to use your language and interface of choice.

 

Welcome SAS Visual Data Mining and Machine Learning on SAS Viya! At its core, SAS Viya is built upon a common in-memory analytic framework, using ‘actions’. These actions are atomic analytic activities, such as selecting variables, building models, generating results, and outputting score code. The actions or components are modular by design.  We have exposed these actions via SAS Procedures, Python, Java, Lua and RESTful APIs (R will be released soon). No matter the language or interrace, you WILL get the same answers for the same actions, whether you use a procedure or a python call into SAS Viya. Start your analysis in SAS, and then continue it in Python, all using the same in-memory data – no duplication.

 

We have also exposed these analytic actions with the Visual Analytics framework. Now you can build a two-layer Neural Network using LBFGS and compare it to a Gradient Boosting model with 500 trees, all within an interactive environment.

 

There’s so much more to this offering. We’ve enhanced our capabilities in autotuning so that you intelligently search the hyperparameter space for the best combination of values that addresses the model objective – that is, misclassification, Lift, KS, and so on. We’ve added in capabilities in high-frequency analytics  like Robust PCA (RPCA), Moving Window PCA, and the capability to detect outliers using Support Vector Data Description (SVDD).

 

Feel free to come meet with myself and other folks in R&D at SAS Global Forum this year. You will see this exciting new update on full display, and in many whitepapers to follow.

 

P.S. the complete set of analytics supported in this release are as follows:

 

Data Wrangling

Modeling

Binning

Logistic Regression

Cardinality

Linear Regression

Imputation

Generalized Linear Models

Transformations

Nonlinear Regression

Transpose

Ordinary Least Squares Regression

SQL

Partial Least Squares Regression

Sampling

Quantile Regression

Variable Selection

Decision Trees

Principal Components Analysis (PCA)

Forest

K-Means Clustering

Gradient Boosting

Moving Window PCA

Neural Network

Robust PCA

Support Vector Machines

 

Factorization Machines

 

Network / Community Detection

 

Text Mining

 

Support Vector Data Description

 

 

SAS Employee
Posts: 153

Re: Machine Learning using SAS code, Python and Visual Analytics -- all in one place! Viya!

Great post, Jonathan.

 

Thanks,

Doug

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