Welcome back! Today, I continue with part 2 of my series on building custom applications on SAS Viya. The goal of this series is to discuss securely integrating custom-written applications into your SAS Viya platform. In the first installment of this series, I outlined my experiences on a recent project.
Because SAS Viya provides distributed computing capabilities, customers wonder how it compares to SAS Grid Manager. SAS® Grid Manager and SAS® Viya® implement distributed computing according to different computational patterns.
This series is geared towards walking you through a piece of the puzzle of operationalizing your analytics: securely integrating your custom applications into your SAS Viya platform. This is particularly useful after exposing your analytics as HTTP REST APIs, as we'll see an intentionally brief example of in this first
Analytics is playing an increasingly strategic role in the ongoing digital transformation of organizations today. However, to succeed and scale your digital transformation efforts, it is critical to enable analytics skills at all tiers of your organization. In a recent blog post covering 4 principles of analytics you cannot ignore,
I recently showed how to use simulation to estimate the power of a statistical hypothesis test. The example (a two-sample t test for the difference of means) is a simple SAS/IML module that is very fast. Fast is good because often you want to perform a sequence of simulations over
An embedding model is a way to reduce the dimensionality of input data, such as images. Consider this to be a type of data preparation applied to image analysis. When an embedding model is used, input images are converted into low-dimensional vectors that can be more easily used by other computer vision tasks. The key to good embedding is to train the model so that similar images are converted to similar vectors.
The iml action was introduced in Viya 3.5. As shown in a previous article, the iml action supports ways to implement the map-reduce paradigm, which is a way to distribute a computation by using multiple threads. The map-reduce paradigm is ideal for “embarrassingly parallel” computations, which are composed of many
Is there programmatic way to reduce memory requirements of a CAS table in SAS® Viya®? Yes! SAS' Steven Sober shows you how.
A previous article introduces the MAPREDUCE function in the iml action. (The iml action was introduced in Viya 3.5.) The MAPREDUCE function implements the map-reduce paradigm, which is a two-step process for distributing a computation to multiple threads. The example in the previous article adds a set of numbers by
The iml action in SAS Viya (introduced in Viya 3.5) provides a set of general programming tools that you can use to implement a custom parallel algorithm. This makes the iml action different than other Viya actions, which use distributed computations to solve specific problems in statistics, machine learning, and
Are you looking for a specific CAS action to use in your project? Maybe you need to create a linear or logistic regression and can't seem to find the CAS action? In this post in the Getting Started with Python Integration to SAS® Viya® series, we are going to look
You may be familiar with the online text Forecasting: Principles and Practice, by two of the very top contributors in the field, Rob Hyndman and George Athanasopoulos. (Both are at Monash University in Australia. Rob was longtime Editor-in-Chief of the International Journal of Forecasting, and George is currently President of
In the second post of the Getting Started with Python Integration to SAS® Viya® series we will learn about Working with CAS Actions and CASResults Objects. CAS actions are commands sent to the CAS server to run a task, and CASResults objects contain information returned from the CAS server. This
CAS Actions and Action Sets - a brief intro - A quick introduction about the distributed CAS server in SAS® Viya®. Index of articles on Getting Started with Python Integration to SAS Viya. Making a Connection - An introduction to SAS Viya and the massively parallel processing CAS engine, and
In the preceding two posts, we looked at issues around interpretability of modern black-box machine-learning models and introduced SAS® Model Studio within SAS® Visual Data Mining and Machine Learning. Now we turn our attention to programmatic interpretability.
It’s official: NASA no longer builds spaceships. They’ve outsourced that task. According to NASA administrator Jim Bridenstine, "We're going with commercial partners. NASA is not purchasing, owning and operating the hardware. We're buying the service." Why? Because NASA needs to focus on exploring space, not building the transportation to get
A previous article provides an introduction and overview of the iml action, which is available in SAS Viya 3.5. The article compares the iml action to PROC IML and states that most PROC IML programs can be modified to run in the iml action. This article takes a closer look
This article introduces the iml action, which is available in SAS Viya 3.5. The iml action supports most of the same syntax and functionality as the SAS/IML matrix language, which is implemented in PROC IML. With minimal changes, most programs that run in PROC IML also run in the iml
In the second of a three-part series of posts, SAS' Funda Gunes and her colleague Ricky Tharrington summarize model-agnostic model interpretability in SAS Viya.
1. Python SWAT en SAS Viya 3.5 El paquete SAS Scripting Wrapper for Analytics Transfer (SWAT) para Python se lanzó por primera vez con SAS Viya 3.1, es una interfaz de Python para SAS Cloud Analytics Services (CAS), la parte central del framework de SAS Viya. Dado que CAS se puede
Let's talk about using DLPy to model employee retention through a survival analysis model. Survival analysis is used to model time-to-event. Examples of time-to-event include the time until an employee leaves a company, the time until a disease goes into remission, or the time until a mechanical part fails. The
Welcome to the first post for the Getting Started with Python Integration to SAS Viya series! With the popularity of the Python programming language for data analysis and SAS Viya's ability to integrate with Python, I thought, why not create tutorials for users integrating the two? To begin the series
Have you heard that SAS offers a collection of new, high-performance CAS procedures that are compatible with a multi-threaded approach? The free e-book Exploring SAS® Viya®: Data Mining and Machine Learning is a great resource to learn more about these procedures and the features of SAS® Visual Data Mining and
Stored processes were a very popular feature in SAS 9.4. They were used in reporting, analytics and web application development. In Viya, the equivalent feature is jobs. Using jobs the same way as stored processes was enhanced in Viya 3.5. In addition, there is preliminary support for promoting 9.4 stored processes to Viya. In this post, I will examine what is sure to be a popular feature for SAS 9.4 users starting out with Viya.
SAS' Kris Stobbe shows how you can predict survival rates of Titanic passengers with a combination of both Python and CAS using SWAT, then see how the models performed.
In this blog, I will show you how a Viya administrator can track and control resource usage of personal caslibs.
SAS Viya。這是一個全新的雲端開放式綜合平臺，代表了SAS新一代的分析架構。SAS Viya能幫助縮短從早期分析探索，到後期領域議題價值實現所需要的時間，它將是包括機器學習等衆多SAS産品的基礎，幫助加速應對各類資料科學的挑戰。
This blog is part of a series on SAS Visual Data Mining and Machine Learning (VDMML). If you're new to SAS VDMML and you want a brief overview of the features available, check out my last blog post! This blog will discuss types of missing data and how to use imputation
概要 第一回の「CASサーバーとSWATパッケージ」に続き、第二回としてCASのアクションセットの活用やCASサーバーへのデータ読み込みなどの基本操作の方法について紹介します。 アクションセットについて CASサーバー上での分析作業を開始する前に、“アクションセット”という重要な概念に関して紹介します。 アクションセットは、関連する機能を実行するアクションの論理的なグループです。 SAS Viyaでは、関数のことを「アクション」、関連する関数のグループを「アクションセット」と呼んでいます。アクションでは、サーバーのセットアップに関する情報を返したり、データをロードしたり、高度な分析を実行するなど、さまざまな処理を実行できます。 アクションセットを使ってみましょう それでは、サンプルコードを使いながら、SAS Viyaのアクションセットでデータの読み込みからプロットまでの一連の操作を説明します。 ・データの読み込み CASサーバーにデータを読み込むには二つの方法があります。一つはread.csv()でcsvファイルをRデータフレームの形で読み込んだ上で、as.casTable()を使用する方法です。この関数はデータをRのデータフレームからCASテーブルにアップロードすることができます。今回の例では金融関連のサンプルデータhmeqを使って紹介します。 library("swat") conn <- CAS(server, port, username, password, protocol = "http") hmeq_data <- read.csv(“hmeq.csv”) hmeq_cas <- as.casTable(conn, hmeq) もう一つはcas.read.csv()を使って、ローカルからファイルを読み込んで、そのままCASサーバーにアップロードする方法です。仕組みとしては、一つ目の方法と大きくは変わりません。 hmeq_cas <- cas.read.csv(conn, hmeq) as.casTable()或いはcas.read.csv()からの出力はCASTableオブジェクトです。その中に、接続情報、作成されたテーブルの名前、テーブルが作成されたcaslib（CASライブラリ）、およびその他の情報が含まれます。 Rのattributes()関数を使えば中身を確認できます。 attributes(hmeq_cas) $conn CAS(hostname=server, port=8777, username=user, session=ca2ed63c-0945-204b-b4f3-8f6e82b133c0, protocol=http) $tname [1] "IRIS" $caslib [1] "CASUSER(user)"
While growing up in the 80's, I watched The Golden Girls on TV with my Grandma Betty. Now, when my sister visits, we binge watch reruns on TV Land. I was excited when I saw for this Halloween, you could buy Golden Girls costumes! Too bad they sold out right
