8月 202021

This article was co-written by Marinela Profi, Product Marketing Manger for AI, Data Science and Open-Source. Check out her blog profile for more information.

Artificial Intelligence (AI) is changing the way people and organizations improve decision-making and move about their lives – from text translation, to chatbots and predictive analytics. However, many organizations are struggling to realize its potential as model deployment processes remain disconnected, creating unforeseen headaches and manual work. Additionally, other requirements like performance monitoring, retraining, and integration into core business processes must be streamlined for optimal teamwork and resource usage.

SAS and Microsoft are partnering to inspire greater trust and confidence in every decision, by driving innovation and proven AI in the cloud. With a combined product roadmap, SAS and Microsoft are working tirelessly to improve offerings and connectivity between SAS Viya and Microsoft Azure environments across industries. That’s why we are especially excited to announce SAS Viya users can now publish SAS and open-source models in Azure Machine Learning.

The SAS and Microsoft team built a tightly integrated connection between SAS Model Manager and Azure Machine Learning to register, validate, and deploy SAS and open-source models to Azure Machine Learning with just a few clicks. From there, data scientists can enrich their applications with SAS or open-source models within their Azure environment.

This integration will enable users to:

1) Extend SAS models stored in SAS Model Manager into the Azure Machine Learning registry, offering more opportunities for collaboration across the enterprise.

2) Deploy SAS and open-source models from SAS Model Manager to Azure Machine Learning on the same Azure Kubernetes cluster you have already set up in Azure Machine Learning. Before deploying the model, you can validate the model and ensure it meets your criteria.

3) Seamlessly connect your SAS Viya and Microsoft environments without the hassle of verifying multiple licenses with single sign-on authentication via Azure Active Directory (Azure AD).

Get started

Step 1: To get started, use Azure AD for simplified SAS Viya access.

Step 2: SAS Model Manager governs, deploys, and monitors all types of SAS and open-source models (i.e., Python, R). On the home page, you can see the projects you and your team are working on in addition to “What’s new” and “How to” videos with the latest updates.

Step 3: Compare different models to identify the most accurate “champion model.” Deploy the model throughout the Microsoft ecosystem from cloud to edge with customizable runtimes, centralized monitoring, and management capabilities.

Step 4: Using the provided artifacts, Azure Machine Learning creates executable containers supporting SAS and open-source models. You can use the endpoints created through model deployment for the scoring of the data.

Step 5: Schedule SAS Model Manager to detect model drift and automatically retrain models in case of poor performance or bias detection.

Discover more

If you want to know more about SAS Model Manager and our partnership with Microsoft, check out the resources below:

“What’s New with SAS Model Manager” article series to find out the latest and greatest updates.
SAS Viya on Azure to solve 100 different use cases on Data for Good, industries and startups.

Let us know what you think!

We would love to hear what you think about this new experience and how we can improve it. If you have any feedback for the team, please share your thoughts and ideas in the comments section below.

Deploying SAS and open-source models to Azure Machine Learning has never been easier was published on SAS Users.

8月 182021

When people think about sports, many things may come to mind: Screaming fans, the intensity of the game and maybe even the food. Data doesn’t usually make the list. But what people may not realize is that data is behind everything people love about sports. It can help determine how [...]

4 ways analytics are enhancing sports was published on SAS Voices by Olivia Ojeda

7月 222021

How do you convince decision makers in your enterprise to give a machine learning (ML) project the green light?

You might be super excited about machine learning – as many of us are – and might think that this stuff should basically sell itself! The value proposition can seem totally obvious when you are already invested in it. The improvement to current operations is a "no-brainer." And the core ML technology is nifty as heck.

But to get traction for a new initiative, to sell it to decision makers, you need to take a step back from the excitement that you feel and tell a simple, non-technical business story that is sober rather than fervent.

Start with an elevator pitch

99.5% of our direct mail is ineffective. Only half a percent respond.

If we can lower that nonresponse rate to 98.5% — and increase the response rate to 1.5% — that would mean a projected $500,000 increase in annual profit, tripling the ROI of the marketing campaigns. I can show you the arithmetic in detail.

We can use machine learning to hone down the size of our mailings by targeting the customers more likely to respond. This should cut costs about three times the amount that it will decrease revenue, giving us the gains and ROI I just mentioned.

A short pitch like this is the best place to start before asking for questions. Get straight to the point – the business value and the bottom line – and then see where your colleagues are coming from. Remember, they're not necessarily excited about ML, so in this early stage, it is really, really easy to bore them. That’s why you must lead with the value and then get into the ML technology only to the degree necessary to establish credibility.

Keep your pitch focused on accomplishing these three things

  1. Your pitch must lead with the value proposition, expressed in business terms without any real details about ML, models, or data. Nothing about how ML works, only the actionable value that it delivers. Focus on the functional purpose, the operational improvement gained by model deployment – and yet, in this opening, don't use the words "model" or "deployment."
  2. Your pitch must estimate a performance improvement in terms of one or two key performance indicators (KPIs) such as response rate, profit, ROI, costs, or labor/staff requirements. Express this potential result in simple terms. For example, the profit curve of a model is “TMI” (Too Much Information) – it introduces unnecessary complexity during this introductory pitch. Instead, just show a bar chart with only two bars to illustrate the potential improvement. Stick with the metrics that matter, the ones people care about — that is, the ones that actually drive business decisions at your company. Make the case that the performance improvement more than justifies the expense of the ML project. Don't get into predictive model performance measures such as lift.
  3. Stop and listen -- keep your pitch short and then open the conversation. Realize that your pitch isn't the conclusion but rather a catalyst to begin a dialogue. By laying out the fundamental proposition and asking them to go next, you get to find out which aspects are of concern and which are of interest, and you get a read on their comfort level with ML or with analytics in general.

So, does the wondrous technology of machine learning itself even matter in this pitch? Can you really sell ML without getting into ML? Well, yes, it does matter, and usually you will get into it, eventually. But you need to interactively determine when to do so, to what depth, and at what pace.

With machine learning, leading with the scientific virtues and quantitative capabilities of the technology that you are selling – predictive modeling algorithms, the idea of learning from data, probabilities, and so on – is like pitching the factory rather than the sausage. Instead, lead with the business value proposition.

It's more common than you may realize for the business professional to whom you're speaking to feel nervous about their own ability to understand analytical technology. The elevator-pitch format serves as an antidote to this type of "tech aversion." Lead with a simple story about how value is delivered or how processes will improve.

These tactics for green lighting compose just one part of machine learning leadership. For machine learning projects to succeed, a very particular leadership practice must be followed. To fully dive in, enroll in my SAS Business Knowledge Series course, Machine Learning Leadership and Practice – End-to-End Mastery. (This article is based on one of the course’s 142 videos.) I developed this curriculum to empower you to generate value with machine learning, whether you work as a techie, a business leader, or some combination of the two. This course delivers the end-to-end expertise that you need, covering both the core technology and the business-side practice. Why cover both sides? Because both sides need to learn both sides! Click here for more details, the full syllabus, and to enroll.

Getting the green light for a machine learning project was published on SAS Users.

7月 212021

In my new book, I explain how segmentation and clustering can be accomplished in three ways: coding in SAS, point-and-click in SAS Visual Statistics, and point-and-click in SAS Visual Data Mining and Machine Learning using SAS Model Studio. These three analytical tools allow you to do many diverse types of segmentation, and one of the most common methods is clustering. Clustering is still among the top 10 machine learning methods used based on several surveys across the globe.

One of the best methods for learning about your customers, patrons, clients, or patients (or simply observations in almost any data set) is to perform clustering to find clusters that have similar within-cluster characteristics and each cluster has differing combinations of attributes. You can use this method to aid in understanding your customers or profile various data sets. This can be done in an environment where SAS and open-source software work in a unified platform seamlessly. (While open source is not discussed in my book, stay tuned for future blog posts where I will discuss more fun and exciting things that should be of interest to you for clustering and segmentation.)

Let’s look at an example of clustering. The importance of looking at one’s data quickly and easily is a real benefit when using SAS Visual Statistics.

Initial data exploration and preparation

To demonstrate the simplicity of clustering in SAS Visual Statistics, the data set CUSTOMERS is used here and also throughout the book. I have loaded the CUSTOMERS data set into memory, and it is now listed in the active tab. I can easily explore and visualize this data by right-mouse-clicking and selecting Actions and then Explore and Visualize. This will take you to the SAS Visual Analytics page.

I have added four new compute items by taking the natural logarithm of four attributes and will use these newly transformed attributes in a clustering.

Performing simple clustering

Clustering in SAS Visual Statistics can be found by selecting the Objects icon on the left and scrolling down to see the SAS Visual Statistics menus as seen below. Dragging the Cluster icon onto the Report template area will allow you to use that statistic object and visualize the clusters.

Once the Cluster object is on the template, adding data items to the Data Roles is simple by checking the four computed data items.

Click the OK icon, and immediately the four data items that are being clustered will look like the report below where five clusters were found using the four data items.

There are 105,456 total observations in the data set, however, only 89,998 were used for the analysis. Some observations were not used due to the natural logarithm not being able to be computed. To see how to handle that situation easily, please pick up a copy of Segmentation Analytics with SAS Viya. Let me know if you have any questions or comments.



Clustering made simple was published on SAS Users.

5月 112021

It’s safe to say that SAS Global Forum is a conference designed for users, by users. As your conference chair, I am excited by this year’s top-notch user sessions. More than 150 sessions are available, many by SAS users just like you. Wherever you work or whatever you do, you’ll find sessions relevant to your industry or job role. New to SAS? Been using SAS forever and want to learn something new? Managing SAS users? We have you covered. Search for sessions by industry or topic, then add those sessions to your agenda and personal calendar.

Creating a customizable agenda and experience

Besides two full days of amazing sessions, networking opportunities and more, many user sessions will be available on the SAS Users YouTube channel on May 20, 2021 at 10:00am ET. After you register, build your agenda and attend the sessions that most interest you when the conference begins. Once you’ve viewed a session, you can chat with the presenter. Don’t know where to start? Sample agendas are available in the Help Desk.

For the first time, proceedings will live on SAS Support Communities. Presenters have been busy adding their papers to the community. Everything is there, including full paper content, video presentations, and code on GitHub. It all premiers on “Day 3” of the conference, May 20. Have a question about the paper or code? You’ll be able to post a question on the community and ask the presenter.

Want training or help with your code?

Code Doctors are back this year. Check out the agenda for the specific times they’re available and make your appointment, so you’ll be sure to catch them and get their diagnosis of code errors. If you’re looking for training, you’ll be quite happy. Training is also back this year and it’s free! SAS instructor-led demos will be available on May 20, along with the user presentations on the SAS Users YouTube channel.

Chat with attendees and SAS

It is hard to replicate the buzz of a live conference, but we’ve tried our best to make you feel like you’re walking the conference floor. And we know networking is always an important component to any conference. We’ve made it possible for you to network with colleagues and SAS employees. Simply make your profile visible (by clicking on your photo) to connect with others, and you can schedule a meeting right from the attendee page. That’s almost easier than tracking down someone during the in-person event.

We know the exhibit hall is also a big draw for many attendees. This year’s Innovation Hub (formerly known as The Quad) has industry-focused booths and technology booths, where you can interact in real-time with SAS experts. There will also be a SAS Lounge where you can learn more about various SAS services and platforms such as SAS Support Communities and SAS Analytics Explorers.

Get started now

I’ve highlighted a lot in this blog post, but I encourage you to view this 7-minute Innovation Hub video. It goes in depth on the Hub and all its features.

This year there is no reason not to register for SAS Global Forum…and attend as few or as many sessions as you want. Why? Because the conference is FREE!

Where else can you get such quality SAS content and learning opportunities? Nowhere, which is why I encourage you to register today. See you soon!

SAS Global Forum: Your experience, your way was published on SAS Users.

4月 062021

Almost a year ago, in the depths of despair after the first 100 days of the COVID-19 pandemic, I wrote a post on how government agencies could consider their options using a framework called Respond, Recover and Reimagine. Now, with greater vaccine availability and mass vaccination by governments – in [...]

Does the public sector need decisioning? was published on SAS Voices by Lee Ann Dietz

2月 252021

The people, the energy, the quality of the content, the demos, the networking opportunities…whew, all of these things combine to make SAS Global Forum great every year. And that is no exception this year.

Preparations are in full swing for an unforgettable conference. I hope you’ve seen the notifications that we set the date, actually multiple dates around the world so that you can enjoy the content in your region and in your time zone. No one needs to set their alarm for 1:00am to attend the conference!

Go ahead and save the date(s)…you don’t want to miss this event!

Content, content, content

We are working hard to replicate the energy and excitement of a live conference in the virtual world. But we know content is king, so we have some amazing speakers and content lined up to make the conference relevant for you. There will be more than 150 breakout sessions for business leaders and SAS users, plus the demos will allow you to see firsthand the innovative solutions from SAS, and the people who make them. I, for one, am looking forward to attending live sessions that will allow attendees the opportunity to ask presenters questions and have them respond in real time.

Our keynote speakers, while still under wraps for now, will have you on the edge of your seats (or couches…no judgement here!).

Networking and entertainment

You read that correctly. We will have live entertainment that'll have you glued to the screen. And you’ll be able to network with SAS experts and peers alike. But you don’t have to wait until the conference begins to network, the SAS Global Forum virtual community is up and running. Join the group to start engaging with other attendees, and maybe take a guess or two at who the live entertainment might be.

A big thank you

We are working hard to bring you the best conference possible, but this isn’t a one-woman show. It takes a team, so I would like to introduce and thank the conference teams for 2021. The Content Advisory Team ensures the Users Program sessions meet the needs of our diverse global audience. The Content Delivery Team ensures that conference presenters and authors have the tools and resources needed to provide high-quality presentations and papers. And, finally, the SAS Advisers help us in a multitude of ways. Thank you all for your time and effort so far!

Registration opens in April, so stay tuned for that announcement. I look forward to “seeing” you all in May.

What makes SAS Global Forum great? was published on SAS Users.

1月 252021

Safety, efficacy, speed and costs must all be prioritized and balanced in the delivery of life-changing therapies to patients. A drug that's quickly and cost-efficiently delivered to market, but isn’t effective and safe is unacceptable. An effective, safe drug that doesn’t get to patients in time to save lives has [...]

The evolving role of AI in drug safety was published on SAS Voices by Cameron McLauchlin

1月 182021

What if you had a technology solution that creates a real-time link between the customer demand signal and what's happening on the ground? What if plans that are being steered centrally could  finally be connected to every shipping lane, while simultaneously, creating cost saving carrier adjustments? The first-of-its kind integration [...]

SAS and C.H. Robinson are rewriting the rules of transportation planning and management was published on SAS Voices by Charlie Chase

1月 112021

On The DO Loop blog, I write about a diverse set of topics, including statistical data analysis, machine learning, statistical programming, data visualization, simulation, numerical analysis, and matrix computations. In a previous article, I presented some of my most popular blog posts from 2020. The most popular articles often deal with elementary or familiar topics that are useful to almost every data analyst.

However, among last year's 100+ articles are many that discuss advanced topics. Did you make a New Year's resolution to learn something new this year? Here is your chance! The following articles were fun to write and deserve a second look.

Machine learning concepts

Relationship between a threshold value and true/false negatives and positives

Statistical smoothers

Bilinear interpolation of 12 data values

I write a lot about scatter plot smoothers, which are typically parametric or nonparametric regression models. But a SAS customer wanted to know how to get SAS to perform various classical interpolation schemes such as linear and cubic interpolations:

SAS Viya and parallel computing

SAS is devoting tremendous resources to SAS Viya, which offers a modern analytic platform that runs in the cloud. One of the advantages of SAS Viya is the opportunity to take advantage of distributed computational resources. In 2020, I wrote a series of articles that demonstrate how to use the iml action in Viya 3.5 to implement custom parallel algorithms that use multiple nodes and threads on a cluster of machines. Whereas many actions in SAS Viya perform one and only one task, the iml action supports a general framework for custom, user-written, parallel computations:

The map-reduce functionality in the iml action

  • The map-reduce paradigm is a two-step process for distributing a computation. Every thread runs a function and produces a result for the data that it sees. The results are aggregated and returned. The iml action supports the MAPREDUCE function, which implements the map-reduce paradigm.
  • The parallel-tasks paradigm is a way to run independent computations concurrently. The iml action supports the PARTASKS function, which implements the map-reduce paradigm.

Simulation and visualization

Decomposition of a convex polygon into triangles

Generate random points in a polygon

Your turn

Did I omit one of your favorite blog posts from The DO Loop in 2020? If so, leave a comment and tell me what topic you found interesting or useful. And if you missed some of these articles when they were first published, consider subscribing to The DO Loop in 2021.

The post Blog posts from 2020 that deserve a second look appeared first on The DO Loop.