4月 102019
 

In numerical linear algebra, there are often multiple ways to solve a problem, and each way is useful in various contexts. In fact, one of the challenges in matrix computations is choosing from among different algorithms, which often vary in their use of memory, data access, and speed. This article describes four ways to perform the sum of squares and crossproducts matrix, which is usually abbreviated as the SSCP matrix. The SSCP matrix is an essential matrix in ordinary least squares (OLS) regression. The normal equations for OLS are written as (X`*X)*b = X`*Y, where X is a design matrix, Y is the vector of observed responses, and b is the vector of parameter estimates, which must be computed. The X`*X matrix (pronounced "X-prime-X") is the SSCP matrix and the topic of this article.

If you are performing a least squares regressions of "long" data (many observations but few variables), forming the SSCP matrix consumes most of the computational effort. In fact, the PROC REG documentation points out that for long data "you can save 99% of the CPU time by reusing the SSCP matrix rather than recomputing it." That is because the SSCP matrix for an n x p data matrix is a symmetric p x p matrix. When n ≫ p, forming the SSCP matrix requires computing with a lot of rows. After it is formed, it is relatively simple to solve a p x p linear system.

SSCP as a matrix computation

Conceptually, the simplest way to compute the SSCP matrix is by multiplying the matrices in an efficient manner. This is what the SAS/IML matrix language does. It recognizes that X`*X is a special kind of matrix multiplication and uses an efficient algorithm to form the product. However, this approach requires that you be able to hold the entire data matrix in RAM, which might not be possible when you have billions of rows.

The following SAS DATA Step creates a data matrix that contains 426 rows and 10 variables. The PROC IML step reads the data into a matrix and forms the SSCP matrix:

/* remove any rows that contain a missing value:
   https://blogs.sas.com/content/iml/2015/02/23/complete-cases.html */
data cars;
set sashelp.cars;
if not nmiss(of _numeric_);
run;
 
proc iml;
use cars;  read all var _NUM_ into X[c=varNames]; close;
/* If you want, you can add an intercept column: X = j(nrow(X),1,1) || X; */
n = nrow(X);
p = ncol(X);
SSCP = X`*X;         /* 1. Matrix multiplication */
print n p, SSCP;

Although the data has 426 rows, it only has 10 variables. Consequentially, the SSCP matrix is a small 10 x 10 symmetric matrix. (To simplify the code, I did not add an intercept column, so technically this SSCP matrix would be used for a no-intercept regression.)

SSCP as an array of inner product computations

The (i,j)th element of the SSCP matrix is the inner product of the i_th column and the j_th column. In general, Level 1 BLAS computations (inner products) are not as efficient as Level 3 computations (matrix products). However, if you have the data variables (columns) in an array of vectors, you can compute the p(p+1)/2 elements of the SSCP matrix by using the following loops over columns. This computation assumes that you can hold at least two columns in memory at the same time:

/* 2. Compute SSCP[i,j] as an inner-product of i_th and j_th columns */
Y = j(p, p, .);
do i = 1 to p;
   u = X[,i];             /* u = i_th column */
   do j = 1 to i;
      v = X[,j];          /* v = j_th column */
      Y[i,j] = u` * v;   
      Y[j,i] = Y[i,j];    /* assign symmetric element */
   end;
end;

SSCP as a sum of outer products of rows

The third approach is to compute the SSCP matrix as a sum of outer products of rows. Before I came to SAS, I considered the outer-product method to be inferior to the other two. After all, you need to form n matrices (each p x p) and add these matrices together. This did not seem like an efficient scheme. However, when I came to SAS I learned that this method is extremely efficient for dealing with Big Data because you never need to keep more than one row of data into memory! A SAS procedure like PROC REG has to read the data anyway, so as it reads each row, it also forms outer product and updates the SSCP. When it finishes reading the data, the SSCP is fully formed and ready to solve!

I've recently been working on parallel processing, and the outer-product SSCP is ideally suited for reading and processing data in parallel. Suppose you have a grid of G computational nodes, each holding part of a massive data set. If you want to perform a linear regression on the data, each node can read its local data and form the corresponding SSCP matrix. To get the full SSCP matrix, you merely need to add the G SSCP matrices together, which are relatively small and thus cheap to pass between nodes. Consequently, any algorithm that uses the SSCP matrix can greatly benefit from a parallel environment when operating on Big Data. You can also use this scheme for streaming data.

For completeness, here is what the outer-product method looks like in SAS/IML:

/* 3. Compute SSCP as a sum of rank-1 outer products of rows */
Z = j(p, p, 0);
do i = 1 to n;
   w = X[i,];           /* Note: you could read the i_th row; no need to have all rows in memory */
   Z = Z + w` * w;
end;

For simplicity, the previous algorithm works on one row at a time. However, it can be more efficient to process multiple rows. You can easily buffer a block of k rows and perform an outer product of the partial data matrix. The value of k depends on the number of variables in the data, but typically the block size, k, is dozens or hundreds. In a procedure that reads a data set (either serially or in parallel), each operation would read k observations except, possibly, the last block, which would read the remaining observations. The following SAS/IML statements loop over blocks of k=10 observations at a time. You can use the FLOOR-MOD trick to find the total number of blocks to process, assuming you know the total number of observations:

/* 4. Compute SSCP as the sum of rank-k outer products of k rows */
/* number of blocks: https://blogs.sas.com/content/iml/2019/04/08/floor-mod-trick-items-to-groups.html */
k = 10;                                /* block size */
numBlocks = floor(n / k) + (mod(n, k) > 0);  /* FLOOR-MOD trick */
W = j(p, p, 0);
do i = 1 to numBlocks;
   idx = (1 + k*(i-1)) : min(k*i, n);  /* indices of the next block of rows to process */
   A = X[idx,];                        /* partial data matrix: k x p */
   W = W + A` * A;
end;

All computations result in the same SSCP matrix. The following statements compute the sum of squares of the differences between elements of X`*X (as computed by using matrix multiplication) and the other methods. The differences are zero, up to machine precision.

diff = ssq(SSCP - Y) || ssq(SSCP - Z) || ssq(SSCP - W);
if max(diff) < 1e-12 then 
   print "The SSCP matrices are equivalent.";
print diff[c={Y Z W}];

Summary

In summary, there are several ways to compute a sum of squares and crossproducts (SSCP) matrix. If you can hold the entire data in memory, a matrix multiplication is very efficient. If you can hold two variables of the data at a time, you can use the inner-product method to compute individual cells of the SSCP. Lastly, you can process one row at a time (or a block of rows) and use outer products to form the SSCP matrix without ever having to hold large amounts of data in RAM. This last method is good for Big Data, streaming data, and parallel processing of data.

The post 4 ways to compute an SSCP matrix appeared first on The DO Loop.

4月 092019
 

Natural language understanding (NLU) is a subfield of natural language processing (NLP) that enables machine reading comprehension. While both understand human language, NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate human language on its own. NLU is designed for communicating with non-programmers – to understand their intent and act on it. NLU algorithms tackle the extremely complex problem of semantic interpretation – that is, understanding the intended meaning of spoken or written language, with all the subtleties, of human error, such as mispronunciations or fragmented sentences.

How does it work?

After your data has been analyzed by NLP to identify parts of speech, etc., NLU utilizes context to discern meaning of fragmented and run-on sentences to execute intent. For example, imagine a voice command to Siri or Alexa:

Siri / Alexa play me a …. um song by ... um …. oh I don’t know …. that band I like …. the one you played yesterday …. The Beach Boys … no the bass player … Dick something …

What are the chances of Siri / Alexa playing a song by Dick Dale? That’s where NLU comes in.

NLU reduces the human speech (or text) into a structured ontology – a data model comprising of a formal explicit definition of the semantics (meaning) and pragmatics (purpose or goal). The algorithms pull out such things as intent, timing, location and sentiment.

The above example might break down into:

Play song [intent] / yesterday [timing] / Beach Boys [artist] / bass player [artist] / Dick [artist]

By piecing together this information you might just get the song you want!

NLU has many important implications for businesses and consumers alike. Here are some common applications:

    Conversational interfaces – BOTs that can enhance the customer experience and deliver efficiency.
    Virtual assistants – natural language powered, allowing for easy engagement using natural dialogue.
    Call steering – allowing customers to explain, in their own words, why they are calling rather than going through predefined menus.
    Smart listener – allowing users to optimize speech output applications.
    Information summarization – algorithms that can ‘read’ long documents and summarize the meaning and/or sentiment.
    Pre-processing for machine learning (ML) – the information extracted can then be fed into a machine learning recommendation engine or predictive model. For example, NLU and ML are used to sift through novels to predict which would make hit movies at the box office!

Imagine the power of an algorithm that can understand the meaning and nuance of human language in many contexts, from medicine to law to the classroom. As the volumes of unstructured information continue to grow exponentially, we will benefit from computers’ tireless ability to help us make sense of it all.

Further Resources:
Natural Language Processing: What it is and why it matters

White paper: Text Analytics for Executives: What Can Text Analytics Do for Your Organization?

SAS® Text Analytics for Business Applications: Concept Rules for Information Extraction Models, by Teresa Jade, Biljana Belamaric Wilsey, and Michael Wallis

Unstructured Data Analysis: Entity Resolution and Regular Expressions in SAS®, by Matthew Windham

So, you’ve figured out NLP but what’s NLU? was published on SAS Users.

4月 092019
 

Everyday millions of Americans turn on lights and plug in chargers without a second thought.  Our power is generally so reliable we take its availability completely for granted.  The vast majority of us don’t realize what it takes to deliver energy -  the massive electrical grid that consists of over [...]

Empowering the edge to deliver cost and risk reduction for the electrical grid was published on SAS Voices by Alison Bolen

4月 082019
 

Have you ever wondered if love at first sight really exists? And if it exists, what qualities are people drawn too? Watch any romantic comedy and you’ll see this phenomenon play out on the big screen. Which begs the question, “If it can happen to them why not me?” Let’s [...]

Love at first sight: authentic or absurd? was published on SAS Voices by Melanie Carey

4月 082019
 


As word spreads that SAS integrates with open source technologies, people are beginning to explore how to connect, interact with, and use SAS in new ways. More and more users are examining the possibilities and with this comes questions like: How do I code A, integrate B, and accomplish C?

Documentation is plentiful but is undergoing a makeover. People aren’t sure where to go for help – and that's why we're launching the SAS Developers Community, where you can gather to ask questions and get answers.

The community will mirror the activities in existing SAS Communities: Q&A, library articles, tips, technical discussions, etc. We migrated some content from other boards. For example, we moved the content from the Coding on SAS Viya board to the new community. Additionally, we scoured other boards for content that may be better aligned with developers and moved it. We also created some original content. Any good community needs participation by all, so read on and get the 411 on the new Developers Community.

Who is the target audience?

Developers – data scientists, application developers, analysts, programmers and administrators – who need to access SAS resources and/or run SAS procedures. This audience may or may not have SAS programming skills but need to access and analyze data using SAS.

What can developers expect to find?

The Developers Community provides a forum for collaboration, Q&A, and knowledge and resource sharing. The focus will be on developers using open source languages and technology. The community will create synergy between communities.sas.com, developer.sas.com, and github.com/sassoftware. SAS employees and external users will post how-to articles and other items of interest in the library section of the community. This community will not replace the SAS Programming Communities, rather, it will fill a void for non-SAS programmers who have a need/desire to interact with SAS.

When will the community launch?

The Developers Community is live! The site is public, and we've moved existing artifacts to the community. I am attending SAS Global Forum and will be available to answer questions about the new community from our booth in the Quad. Come by and see me!

Where will the community live?

The Developers Community exists on communities.sas.com, under the Developers Category.

Why do we need a community for developers?

Developers need a centralized place to share ideas, ask and answer questions, and discover resources. Currently developers lack a forum to work through things such as authentication, coding, API use, and integration issues. The community will encourage communication, engagement and leadership. Also, the Developers Community will be tightly integrated with the SAS Developers web site and SAS GitHub resources.

How do we go about creating the community?

After seeding the SAS Developer Community with existing discussions, we'll build out a group of SAS developer experts to help monitor the community. The true magic will happen as questions are asked, discussions transpire, and ideas are shared. But we need to your help too. Here is your call to action.

Share the community with your networks, buddies and even family members who may get something out of chatting it up about how to develop in SAS. The livelihood of the community hinges on user interaction. Our current and future users will thank you for it. And you may make a friend while you're at it.

Launching the Developers Community in SAS Communities was published on SAS Users.

4月 082019
 

Suppose you need to assign 100 patients equally among 3 treatment groups in a clinical study. Obviously, an equal allocation is impossible because the second number does not evenly divide the first, but you can get close by assigning 34 patients to one group and 33 to the others. Mathematically, this is a grade-school problem in integer division: simply assign floor(100/3) patients to each group, then deal with the remainders. The FLOOR function rounds numbers down.

The problem of allocating a discrete number of items to groups comes up often in computer programming. I call the solution the FLOOR-MOD trick because you can use the FLOOR function to compute the base number in each group and use the MOD function to compute the number of remainders. Although the problem is elementary, this article describes two programming tips that you can use in a vectorized computer language like SAS/IML:

  • You can compute all the group sizes in one statement. You do not need to use a loop to assign the remaining items.
  • If you enumerate the patients, you can directly assign consecutive numbers to each group. There is no need to deal with the remainders at the end of the process.

Assign items to groups, patients to treatments, or tasks to workers

Although I use patients and treatment groups for the example, I encountered this problem as part of a parallel programming problem where I wanted to assign B tasks evenly among k computational resources. In my example, there were B = 14 tasks and k = 3 resources.

Most computer languages support the FLOOR function for integer division and the MOD function for computing the remainder. The FLOOR-MOD trick works like this. If you want to divide B items into k groups, let A be the integer part of B / k and let C be the remainder, C = B - A*k. Then B = A*k + C, where C < k. In computer code, A = FLOOR(B/k) and C = MOD(B, k).

There are many ways to distribute the remaining items, but for simplicity let's give an extra item to each of the first C groups. For example, if B = 14 and k = 3, then A = floor(14/3) = 4 and C = 2. To allocate 14 items to 3 groups, give 4 items to all groups, and give an extra item to the first C=2 groups.

In a vector programming language, you can assign the items to each group without doing any looping, as shown in the following SAS/IML program:

/* Suppose you have B tasks that you want to divide as evenly as possible 
   among k Groups. How many tasks should you assign to the i_th group? */
proc iml;
/* B = total number of items or tasks (B >= 0, scalar)
   k = total number of groups or workers (k > 0, scalar)
   i = scalar or vector that specifies the group(s), max(i)<=k
   Return the number of items to allocate to the i_th group */
start AssignItems(B, k, i);
   n = floor(B / k) + (i <= mod(B, k));     /* the FLOOR-MOD trick */
   return(n);
finish;
 
/* Test the code. Assign 14 tasks to 3 Groups. */
nItems = 14;
nGroups = 3;
idx = T(1:nGroups);          /* get number of items for all groups */
Group = char(idx);           /* ID label for each group */
n = AssignItems(nItems, nGroups, idx);
print n[c={"Num Items"} r=Group L="Items per Group"];

The AssignItems function is a "one-liner." The interesting part of the AssignItems function is the binary expression i <= mod(B, k), which is valid even when i is a vector. In this example, the expression evaluates to the vector {1, 1, 0}, which assigns an extra item to each of the first two groups.

Which items are assigned to each group?

A related problem is figuring out which items get sent to which groups. In the example where B=14 and k=3, I want to put items 1-5 in the first group, items 6-10 in the second group, and items 11-14 in the last group. There is a cool programming trick, called the CUSUM-LAG trick, which enables you to find these indices. The following function is copied from my article on the CUSUM-LAG trick. After you find the number of items in each group, you can use the ByGroupIndices function to find the item numbers in each group:

/* Return kx2 matrix that contains the first and last elements for k groups that have sizes 
    s[1], s[2],...,s[k]. The i_th row contains the first and last index for the i_th group. */
start ByGroupIndices( s );
   size = colvec(s);              /* make sure you have a column vector */
   endIdx = cusum(size);          /* locations of ending index */
   beginIdx = 1 + lag(endIdx);    /* begin after each ending index ... */
   beginIdx[1] = 1;               /*    ...and at 1  */
   return ( beginIdx || endIdx );
finish;
 
/* apply the CUSUM-LAG trick to the item allocation problem */
GroupInfo = n || ByGroupIndices(n);
print GroupInfo[r=Group c={"NumItems" "FirstItem" "LastItem"}];

The table shows the number of items allocated to each group, as well as the item indices in each group. You can use the FLOOR-MOD trick to get the number of items and the CUSUM-LAG trick to get the indices. In a vector language, you can implement the entire computation without using any loops.

The post Use the FLOOR-MOD trick to allocate items to groups appeared first on The DO Loop.

4月 082019
 
The catch phrase “everything happens somewhere” is increasingly common these days.  That “somewhere” translates into a location on the Earth; a latitude and longitude.  When one of these “somewhere’s” is combined with many other “somewhere’s”, you quickly have a robust spatial data set that becomes actionable with the right analytic tools.

Opportunities for Spatial Analytics are increasing

In today’s modern world, GPS-enabled devices are ubiquitous, and their use continues to increase daily.  Cell phones, cars, fitness trackers, and cameras are all able to locate and track our position.  As a result, the location analytics market is expected to grow to over USD 16 Billion by 2021, up 17.6% from 2016 [1].

Waldo Tobler, an American-Swiss geographer and cartographer, developed his First Law of Geography based on this concept of everything happening somewhere.  He stated, “Everything is related to everything else, but near things are more related than distant things”[2].  As analytic professionals, we are accustomed to working with these correlations using scatterplots, heatmaps, or clustering models.  But what happens when we add a geographic map into the analysis?

Maps offer the ability to unlock a new level of insight into our data that traditional graphs do not offer: personal connection.  As humans, we naturally relate to our surroundings on a spatial level.   It helps build our perspective and frame of reference through which we view and navigate the world.  We feel a sense of loss when a physical landmark from our childhood – a building, tree, park, or route we used to walk to school – is destroyed or changed from the memories we have of it.  In this sense, we are connected, spatially and emotionally, to our surroundings.

We inherently understand how data relates to the world around us, at some level, just by viewing it on a map.  Whether it is a body of water or a mountain affecting a driving route or maybe a trendy area of a city causing housing prices to increase faster than the local average, a map connects us with these facts intuitively.  We come to these basic conclusions based solely on our experiences in the world and knowledge of the physical landmarks in the map.

One of the best examples of this is the 1854 Cholera outbreak in London.  Dr. John Snow was one of the first to use a map for understanding the origin of an epidemiological outbreak.  He created a map of the affected London neighborhood by plotting the location of all known Cholera deaths.  In addition to the deaths, he also plotted the location of 13 community wells that served as the public water supply.  Using this data, he was able to see a clustering of deaths around a single pump.  Armed with this information, Dr. Snow was able to convince local officials to remove the handle from the Broad Street pump.  Once removed, new cases of Cholera quickly began to diminish.  This helped prove his theory the outbreak’s origin was not air-borne as commonly believed during that time, but rather of a water-borne origin. [3]

1854 London Cholera deaths: Tabular data vs. Coordinate map [3]

Let’s look at how Dr. Snow’s map helped mitigate the outbreak and prove his theory.  The image above compares the data of the recorded deaths and community wells in tabular form to a Coordinate map.  It is obvious from the coordinate map that there is a clustering of points.  Town officials and those familiar with the neighborhood could easily get a sense of where the outbreak was concentrated.  The map told a better story by connecting their personal experience of the area to the locations of the deaths and ultimately to the wells.  Something a data table or traditional graph could not do.

Maps of London Cholera deaths with modern analytic overlays [3]

Today, with the computing power and modern analytic methods available to us, we can take the analysis even further.  The examples above show the same coordinate map with added Voronoi polygon and cluster analysis overlays.  The concentration around the Broad Street pump becomes even clearer, showing why Geographic Maps are an important tool to have in your analytic toolbox.

SAS Global Forum 2019 is being held April 28-May 1, 2019 in Dallas, Texas.  If you are planning to go to this year’s event, be sure to attend one of our presentations on the latest mapping features included in SAS Visual Analytics and BASE SAS.  While you’re there, don’t forget to stop by the SAS Mapping booth located in the QUAD to say ‘Hi!’ and let us help with your spatial data needs.  See you in Dallas!

Introduction to Esri Integration in SAS Visual Analytics

  • Monday, April 29, 4:30-5:30p, Room: Level 1, D162

There’s a Map for That! What’s New and Coming Soon in SAS Mapping Technologies

  • Tuesday April 30, 4:00-4:30p, Room: Level 1, D162

Creating Great Maps in ODS Graphics Using the SGMAP Procedure

  • Wednesday May 01, 11:30a-12:30p, Room: Level 1, D162

[1] https://www.marketsandmarkets.com/Market-Reports/location-analytics-market-177193456.html

[2] https://en.wikipedia.org/wiki/Tobler%27s_first_law_of_geography

[3] https://www1.udel.edu/johnmack/frec682/cholera/

How the 1854 Cholera outbreak showed us the importance of spatial analysis was published on SAS Users.

4月 062019
 

Recently, the North Carolina Human Trafficking Commission hosted a regional symposium to help strengthen North Carolina’s multidisciplinary response to human trafficking. One of the speakers shared an anecdote from a busy young woman with kids. She had returned home from work and was preparing for dinner; her young son wanted [...]

Countering human trafficking using text analytics and AI was published on SAS Voices by Tom Sabo

4月 062019
 

Recently, the North Carolina Human Trafficking Commission hosted a regional symposium to help strengthen North Carolina’s multidisciplinary response to human trafficking. One of the speakers shared an anecdote from a busy young woman with kids. She had returned home from work and was preparing for dinner; her young son wanted [...]

Countering human trafficking using text analytics and AI was published on SAS Voices by Tom Sabo

4月 052019
 

Recently, you may have heard about the release of the new SAS Analytics Cloud. The platform allows fast access to data-science applications in the cloud! Running on the SAS Cloud and using the latest container technology, Analytics Cloud eliminates the need to install, update, or maintain software or related infrastructure.

SAS Machine Learning on SAS Analytics Cloud is designed for SAS and open source data scientists to gain on-demand programmatic access to SAS Viya. All the algorithms provided by SAS Visual Data Mining and Machine Learning (VDMML), SAS Visual Statistics and SAS Visual Analytics are available through the offering. Developers and data scientists access SAS through a programming interface using either the SAS or Python programming languages.

A free trial for Analytics Cloud is available, and registration is simple. The trial environment allows users to manage and collaborate with others, share data, and create runtime models to analyze their data. The system is pre-loaded with sample data for learning, and allows users to upload their own data. My colleague Joe Furbee explains how to register for the trial and takes you on a tour of the system in his article, Zero to SAS in 60 Seconds- SAS Machine Learning on SAS Analytics Cloud.

Luckily, I had the privilege of being the technical writer for the documentation for SAS Analytics Cloud, and through this met two of my now close friends at SAS.

Alyssa Andrews (pictured left) and Mariah Bragg (pictured right) are both Software Developers at SAS, but worked on the UI for SAS Analytics Cloud. Mariah works in the Research and Development (R&D) division of SAS while Alyssa works in the Information Technology (IT) division. As you can see this project ended up being an interesting mix of SAS teams!

As Mariah told me the history, I learned that SAS Analytics Cloud “was a collaborative project between IT and R&D. The IT team presented the container technology idea to Dr. Goodnight but went to R&D because they wanted this idea run like an R&D project.”

As we prepared for the release of SAS Analytics Cloud to the public, I asked Mariah and Alyssa about their experience working on the UI for SAS Analytics Cloud, and about all the work that they had completed to bring this powerful platform to life!


What is SAS Analytics Cloud for you? How do you believe it will help SAS users?

Alyssa: For me, it is SAS getting to do Software as a Service. So now you can click on our SAS Software and it can magically run without having to add the complexity of shipping a technical support agent to the customers site to install a bunch of complex software.

Mariah: I agree. This will be a great opportunity for SAS to unify and have all our SAS products on cloud.

Alyssa: Now, you can trial and then pay for SAS products on the fly without having to go through any complexities.

What did you do on the project as UI Developers?

Alyssa: I was lent out to the SAS Analytics Cloud team from another team and given a tour-of-duty because I had a background in Django (a high-level Python Web design tool) which is another type of API framework you can build a UI on top of. Then I met Mariah, who came from an Angular background, and we decided to build the project on Angular. So, I would say Mariah was the lead developer and I was learning from her. She did more of the connecting to the API backend and building the store part out, and I did more of the tweaks and the overlays.

What is something you are proud of creating for SAS Analytics Cloud?

Mariah: I’m really proud to be a part of something that uses Angular. I think I was one of the first people to start using Angular at SAS and I am so excited that we have something out there that is using this new technology. I am also really proud of how our team works together, and I’m really proud of how we architectured the application. We went through multiple redesigns, but they were very manageable, and we really built and designed such that we could pull out components and modify parts without much stress.

Alyssa: That we implemented good design practices. It is a lot more work on the front-end, but it helps so much not to have just snowflake code (a term used by developers to describe code that isn’t reusable or extremely unique to where it becomes a problem later on and adds weight to the program) floating. Each piece of code is there for a reason, it’s very modular.

What are your hopes for the future of SAS Analytics Cloud?

Alyssa: I hope that it continues to grow and that we add even more applications to this new container technology, so that SAS can move even more into the cloud arena. I hope it brings success. It is a really cool platform, so I can’t wait to hear about users and their success with it.

Mariah:
I agree with Alyssa. I also hope it is successful so that we keep moving into the Cloud with SAS.

Learning more

As a Developmental Editor with SAS Press, it was a new and engaging experience to get to work with such an innovative technology like SAS Analytics Cloud. I was happy I got to work with such an exciting team and I also look forward to what is next for SAS Analytics Cloud.

And as a SAS Press team member, I hope you check out the new way to trial SAS Machine Learning with SAS Analytics Cloud. And while you are learning SAS, check out some of our great books that can help you get started with SAS Studio, like Ron Cody’s Biostatistics by Example Using SAS® Studio and also explore Geoff Der and Brian Everitt’s Essential Statistics Using SAS® University Edition.

Already experienced but want to know more about how to integrate R and Python into SAS? Check out Kevin D. Smith’s blogs on R and Python with SAS Viya. Also take a moment to investigate our new books on using open source R and Python with SAS Viya: SAS Viya: The R Perspective by Yue Qi, Kevin D. Smith, and XingXing Meng and SAS Viya: The Phyton Perspective by Kevin D. Smith and XingXing Meng.

These great books can set you on the right path to learning SAS before you begin your jump into SAS Analytics Cloud, the new way to experience SAS.

SAS® Analytics Cloud—an interview with the women involved was published on SAS Users.