Happy Holidays everyone!  I wanted to bring everyone up to speed on the latest features and innovations released last week in VDMML 8.5. This was part of a larger release of Viya 3.5. In this release, you can now run automated feature engineering and modeling with the click of a button inside Model Studio, or embed ' a data scientist in a box' using the new machine learning pipeline automation api. This API is fully documented with examples on developer.sas.com. You can catch the latest new features in action here in my Snapshot Video. Read on to hear about these new features.   You would be hard pressed to attend a conference, read a whitepaper or blog, or attend a webinar without hearing about automated machine learning (Auto-ML). You do not need to be a data scientist to take advantage of the latest methods in machine learning to identify fraud, predict churn, or classify credit-worthiness. The latest release of VDMML 8.5 provides one-click access to dynamically generate a pipeline for you. This dynamic process will run hundreds of techniques for you to identify potential data quality issues, candidate transformations, and the right machine learning techniques to address your business problem. Tjhe system will keep you updated as it is running your process.     Once the process completes you will be provided with a pipeline that represents the top features and models that were identified.  The beauty of this process is that it is not black box. You have full transparency on the results, and can edit or add in additional techniques. Analyze the most important attributes using Kernal Shap and gain an understanding of how to interpret it using natural language.     We also released 2 new nodes to support automated feature engineering (Feature Machine) and automated modeling (Model Composer).  These nodes allow you complete control over the transformation policies and machine learning models run. Model Composer allows you to run autotuning across multiple algorithms, allowing the processing to take advantage of your compute infrastructure - as the processing runs multiple rounds, priority is given to the most predictive algorithms.     In summary, I am extremely excited to share these new features with everyone. There is so much more to come in this area from SAS, stay tuned more powerful features in upcoming releases.   Jonathan Wexler Advisory Product Manager - SAS AI and ML           ... View more

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     ... View more

Good afternoon everyone.  Glad to see everyone is participating in this community, the activity has really grown!  Based on our recent poll results, I wanted to give you reasons to upgrade to 9.4 Maintenance Release 1 / EM 13.1, which was released in December 2013. 1. 10 new nodes!!!  These cover analytic methods including GLM, Support Vector Machines, Clustering, Principal Components, and Time Series (3 new nodes).  We also added in the Save Data Node, which enables you to export SAS data sets.  Finally, we added in the Open Source Integration node which enables you to embed Open Source R algorithms inside an EM flow. 2. Automatic multi-threading out of the box for many HP nodes, such as Random Forest, Neural Network, SVM, and many others.  Take advantage of those multi-core machines! 3. We have documented our High-Performance procedures for use outside of the EM GUI.  That means you can write code, taking advantage of the multi-threaded procedures that EM utilizes. Our next release of Enterprise Miner will be in August, which coincides with 9.4M2 / 13.2.  We have taken the time to enhance algorithms such as Random Forest, Neural Networks, Regression, and several others.  Look for more announcements. Best, Jonathan Wexler Product Manager - SAS Enterprise Miner ... View more