Model Autotuning, Generative AI | SAS Viya March 2023 Release

Summary

In the April 2023 SAS Viya release highlight show, host Thiago Doza discusses new features with experts. Sasha Karpinsky introduces Microsoft Word integration for SAS, allowing users to embed and update SAS reports in Word documents. Joe Madden presents updates in machine learning, including the new ASTORE model format for neural networks and autotuning capabilities in Light GBM. Alex Vilan covers enhancements in SAS Studio, such as data engineering steps and custom column steps. The show also features part two of an interview with Mary Osborne on generative AI models, exploring their potential and ethical considerations in various applications.

Takeaways

• 😀 The SAS Viya release as of April 2023 includes significant updates and integrations.
• 📈 Integration with Microsoft Word allows for seamless sharing of SAS analytical insights within Word documents.
• 💡 Machine learning updates feature a new ASTORE model format for neural networks, enhancing deployability and scoring speed.
• 🔧 Autotuning capabilities in Light GBM have been introduced to improve efficiency and scalability for large datasets.
• 🎨 SAS Studio has been enhanced with new data engineering steps, custom column steps, and an analyst step for ranking data.
• 🔄 The ability to redeploy Studio flows using SAS and Python programs has been added for greater flexibility.
• 🤖 Generative AI models, like the BERT-based classifier, are being integrated into SAS Viya to improve natural language processing.
• 👩‍🏫 Generative AI has the potential to revolutionize personalized education and product recommendation systems.
• 🏥 Synthetic data generation can aid researchers in modeling rare diseases by expanding data sets for better analysis.
• 🛡️ SAS is taking a cautious approach to generative AI, prioritizing ethical considerations and risk mitigation.
• 📚 Users are advised to verify the information generated by AI tools like chat GPT to ensure accuracy and reliability.

Q & A

What is the main topic of the SAS Viya Release Highlight Show for April 2023?

The main topic is the new features and updates in SAS Viya as of April 2023, including integrations and enhancements in areas like Microsoft Word, machine learning, and SAS Studio.

How does the new Microsoft Word integration in SAS Viya work?

The integration allows users to view and interact with SAS visual analytics reports, insert visual insights directly into Word documents, and update embedded SAS content with the latest data, all without leaving the Word application.

What is the significance of the aStore model format for neural networks in SAS Viya?

The aStore model format is significant because it is easily deployable, more portable, and faster than traditional data step methods, leading to a significant improvement in scoring time for neural networks.

What is the new autotuning capability in Light GBM as discussed in the show?

The new autotuning capability in Light GBM allows for efficiency and scalability for large datasets with many features, and it includes unique parameters like bagging fraction, bagging frequency, lasso, and ridge.

What are the new features introduced in SAS Studio for data engineering?

SAS Studio introduces advanced data engineering capabilities such as the ability to create outputs as views, a new 'rank data' step for assigning rank values, and extended scheduling functionality for Studio flows.

How can users deploy or redeploy Studio flows using SAS and Python programs?

Users can deploy Studio flows as jobs without specifying a scheduling trigger and redeploy jobs that have been previously deployed from a specific Studio flow, with this option also supported for SAS and Python programs.

What is the focus of the interview with Mary Osborne in the show?

The interview focuses on generative AI models, discussing the recent release of a BERT-based classifier in SAS Viya, the concept of generative AI, and its potential impact on everyday life and business.

What are some examples of how generative AI can impact everyday life?

Generative AI can impact everyday life by providing personalized instruction through 'teacher in the box' concepts, curating product information, and generating synthetic data for research purposes, among other applications.

What are the key technologies behind generative AI as discussed in the interview?

The key technologies behind generative AI include large language models based on transformer architecture, such as BERT and GPT, and generative adversarial models for generating synthetic tabular data.

What is SAS's approach to the development and deployment of generative AI models?

SAS is taking a conservative approach to the development and deployment of generative AI models, focusing on trustworthiness, ethical considerations, mitigating risks, and ensuring a strong return on investment for customers.

What advice does Mary Osborne give to casual users of generative AI like chat GPT?

Mary Osborne advises casual users to use generative AI with caution, always verify the information it provides, and be aware that the models can generate very plausible but incorrect or non-existent content.

Outlines

00:00

📈 Introduction to SAS VIA Release Highlights

The video script introduces the SAS VIA release highlight show hosted by Thiago Doza, focusing on updates as of April 2023. It sets the stage for various segments, including Microsoft Word integration in SAS for Microsoft 365 by Sasha Karpinsky, machine learning updates by Joe Madden featuring a new model format for neural networks and autotuning capabilities in Light GBM, and new features in SAS Studio by Alex Vilan, such as data engineering steps, custom column steps, and the ability to deploy or redeploy Studio flows using SAS and Python programs. The script also mentions the continuation of an interview with Mary Osborne about generative AI models.

05:02

🔗 Microsoft Word Integration and Machine Learning Updates

Sasha Karpinsky discusses the new integration of SAS with Microsoft Word, enhancing the ability to share analytical insights within an organization directly from Word documents. Joe Madden introduces advancements in machine learning, specifically the support for the analytical store (ASTORE) in SAS for neural networks, which improves deployability, portability, and speed. Additionally, autotuning for Light GBM is presented, which includes unique parameters to enhance efficiency and scalability for large datasets. The segment also previews new output results in Model Studio, including plots for evaluation and iteration history.

10:04

🛠️ Advanced Data Engineering and Custom Step Extensions

Alex Vilan presents new features in SAS Studio, focusing on advanced data engineering capabilities. These include the ability to create views as outputs, a new 'rank data' step for assigning rank values, and extended scheduling functionality for SAS Studio flows. The segment also covers enhancements to custom steps, such as making input table and column selector controls read-only and hiding their values at runtime, as well as excluding specific columns from previous selections. These updates aim to empower custom step authors to build more powerful and flexible custom steps.

15:06

💡 Generative AI Models and Their Impact

Mary Osborne discusses generative AI models, starting with the release of a BERT-based classifier in SAS VIA. She explains generative AI as a technology for creating content, including images, text, and structured data. Osborne provides examples of generative AI's impact on everyday life, such as personalized education and curated product information. She also touches on business applications like synthetic data generation for rare disease research and the potential for content creation acceleration. The conversation emphasizes the importance of ethical considerations, computational costs, and the potential risks and benefits of generative AI.

20:09

🌐 Ethical Considerations and Future of Generative AI

The discussion continues with ethical concerns and the future of generative AI at SAS. Osborne emphasizes SAS's commitment to developing trustworthy models and methods, taking a conservative approach to novel text generation to mitigate risks. The conversation highlights the importance of an ethical path, considering data sources, privacy, and the potential for model hallucinations. The segment also addresses the high computational costs and the rapid evolution of generative AI, with a focus on ensuring a strong return on investment and reducing risks for customers. Practical advice for users is provided, urging caution and verification of generative AI outputs.

📢 Closing Remarks and Invitation for Future Engagement

Thiago Doza concludes the show by summarizing the updates and inviting viewers to try out the new SAS VIA features. He encourages engagement through likes and subscriptions on the SAS Users YouTube channel and asks for comments on topics for future shows. The script ends with a tease for the next show in May, maintaining viewer interest and engagement.

Keywords

💡SAS Viya

SAS Viya is an advanced analytics platform from SAS that supports a wide range of analytics techniques from descriptive to predictive to prescriptive analytics. In the video, it is highlighted as the main subject with updates and new features being discussed, such as integration with Microsoft Word and enhancements in machine learning capabilities.

💡Microsoft Word Integration

This refers to the new feature in SAS Viya that allows users to integrate SAS visual analytics reports directly into Microsoft Word documents. This integration facilitates sharing analytical insights within an organization without leaving the Word application, as mentioned by Sasha Karpinsky in the script.

💡Machine Learning

Machine learning is a type of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. In the video, Joe Madden discusses updates related to machine learning in SAS Viya, including a new model format for neural networks and autotuning capabilities in Light GBM.

💡Neural Networks

Neural networks are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. The script mentions a new model format for neural networks in SAS Viya, which is the analytical store (ASTORE), allowing for faster scoring times compared to traditional data step methods.

💡Light GBM

Light GBM is a gradient boosting framework that uses tree-based learning algorithms. It is known for its efficiency with large datasets. The script discusses the addition of autotuning capabilities in Light GBM, which will help in optimizing model parameters for better performance.

💡Model Autotuning

Model autotuning is the process of automatically tuning the parameters of a model to improve its performance. In the context of the video, autotuning is being implemented in Light GBM to help users achieve better model fits with less manual effort.

💡SAS Studio

SAS Studio is an integrated development environment (IDE) for SAS that provides code editing, debugging, and submission facilities. Alex Vilan in the script discusses new features in SAS Studio, such as data engineering steps, custom column steps, and the ability to deploy or redeploy Studio flows using SAS and Python programs.

💡Generative AI

Generative AI refers to AI models that can generate new content, such as images, text, or structured data. In the video, Mary Osborne discusses generative AI models, including their potential applications and ethical considerations, highlighting the release of a BERT-based classifier in SAS Viya.

💡BERT (Bidirectional Encoder Representations from Transformers)

BERT is a large language model that is used for natural language processing tasks. It is mentioned in the script as a recent addition to SAS Viya, which allows for more advanced text analytics capabilities compared to traditional rule-based approaches.

💡Synthetic Data

Synthetic data is artificially generated data that mimics the properties of real data. In the context of the video, synthetic data generation is discussed as a way to expand datasets, especially useful for modeling rare events or diseases where real data is scarce.

Highlights

SAS Viya release highlights for April 2023 include integration with Microsoft Word, machine learning updates, and new features in SAS Studio.

Sasha Karpinsky discusses the new Microsoft Word integration for SAS, allowing users to share analytical insights directly within Word documents.

Joe Madden introduces a new model format for neural networks, the analytical store (ASTORE), for faster and more portable model deployment.

Autotuning capabilities in Light GBM are now available for improved efficiency and scalability with large datasets.

Alex Vilan covers new features in SAS Studio, including data engineering steps, custom column steps, and the ability to redeploy Studio flows using SAS and Python programs.

Part two of the interview with Mary Osborne explores generative AI models and their potential impact on everyday life.

Generative AI can provide personalized instruction through 'teacher in a box' technology, enhancing education.

Generative AI can curate product information, offering a more general and trustworthy approach to consumer decisions.

Synthetic data generation is highlighted as a way to expand datasets for rare diseases, improving the foundation for modeling and research.

Large language models and generative adversarial models are key technologies behind generative AI, with applications in natural language processing and structured data.

SAS is taking a conservative approach to generative AI, focusing on ethical considerations and ensuring a strong ROI for customers.

The future of generative AI at SAS involves continued research and development, with an emphasis on trustworthiness and risk mitigation.

The rapid growth of generative AI models like GPT is discussed, with newer versions offering improved capabilities.

Users are advised to approach generative AI with caution, verifying the information it provides to avoid inaccuracies.

The show concludes with a reminder to subscribe to the SAS Users YouTube channel for updates on future episodes.

Transcripts

[Music]

hello everyone and welcome to the SAS

via release highlight show I'm Thiago

Doza and today we'll be talking about

what's new in SAS via as of April

2023 for our rundown segment we have

Sasha karpinsky highlighting Microsoft

Word integration in SAS for Microsoft

365 then we have Joe Madden with a few

updates on machine learning

first he'll tell us about a new aore

model format for neural networks and

then he'll cover some new model

autotuning capabilities in light GBM and

in model Studio Alex Vilan is also back

focusing on different ways to look at

your data Alexi will cover multiple new

features in SAS Studio like data

engineering steps there are two new

custom column steps a new analyst step

for ranking data and the ability to de

deploy or redeploy Studio flows using

SAS and python programs finally in our

release talk segment we have part two of

our interview with Mary Osborne about

generative AI models but that's just an

overview of everything you'll hear about

today why don't we get right into it I

give to you the

[Music]

rundown welcome to the

20233 release highlights for SAS from

Microsoft

365 March

2023 today I'm so excited to introduce

sas's integration with Microsoft Word

available on both web and desktop

versions of word these new features make

it easier than ever to share critical

analytical insights from SAS with the

rest of your

organization you can now View and

interact with SAS visual analytics

reports insert visual insights from SAS

directly into your Word documents and up

update embedded SAS content with the

latest SAS data all without ever leaving

your word

application hi this is Joe Madden and I

have a few exciting machine learning

updates to share with you for the latest

release of SAS viia we'll kick things

off with a big Improvement for neural

networks we are pleased to share that

the analytical store commonly known as a

store is Now supported directly in SAS

Studio the SAS code node or the SWAT

python library today and it'll soon be

available model studio aore is our

preferred format within via for saving

and scoring a model because it's easily

Deployable it's more portable and it's

faster than relying upon the traditional

data step method in this example we have

a neural net running against some

simulated data scoring time is a scale

of magnitude faster with a store

compared to that data step and even when

looking at smaller data sets significant

Improvement is expected so if you've

used a store for other model types this

will be just like what you're familiar

with and remember scoring can be called

from the proc net and proc a store

procedure next we have another evolution

of the light GBM implementation we're

pleased to announce that autot tuning is

now ready for the light grab boost

action fans of light GBM know it's

particularly powerful when you need

efficiency and scalability for large

data sets that it often includes a large

number of features uh so this usage is

very similar to other autotune features

with a couple unique parameters such as

bagging fraction bagging frequency lasso

and

ridge so let's take a look at some of

those results just as you would see with

other autot tuning output you're going

to get nice view into what happened at

each iteration and don't worry if you're

not a programmer this will soon be

brought into model studio and speaking

of model studio and autot tuning we have

new output results that you're going to

want to check out in this example we're

showing gradient boosting enabled for

autotuning set up with the genetic

algorithm for its search

method the two new plots are evaluation

history and iteration history evaluation

history shows a scatter plot across

iterations each point on the plot

represents the various evaluations

performed within an iteration iterations

are denoted by color and represent

unique groupings of hyperparameter

values along with its results and the

solid line represents the best

combination for the objective function

that the autotune process found the

second plot iteration history shows how

time and the objective function spans

with iterations in this case the higher

KS value shows a better model fit as

time increases we'll see that the

objective also

increases while we just have time to

show this in action for graad Boost it

works across all model types so make

sure to give it a try

today hello everyone welcome to sus

Studio release 202 free or free in this

release we introduce a few Advanced Data

engineering capabilities in s

studio let's take a look first of all

for a table step in s studio we add

support of the ability to create an

output as a view so now you can choose

whether your output should be located in

a physical table or it should be created

in a form of view next for s studio

analyst we we introduce a new step T

that is called rank data this tab will

allow you to calculate and assign rank

values to your data

set next we significantly extend the

functionality of scheduling for s studio

flows we introduce a couple of options

on S Studio flows menu first we allow to

deploy s studio flow is a job without

the need to specify a scheduling trigger

and secondly we now also allow to rate

deploy jobs that have been deployed

previously from a specific s studio flow

this option is now also supported for

SAS and python programs in SAS

Studio next let's take a quick look at

the extensions for custom steps

framework for a couple of dynamic

controls that we have in custom steps

input table and column selector we now

allow to make these controls read only

and also allow to hide the values of

these controls at R

time also for column selector control we

add an additional attribute that allows

to exclude specific columns from the

previous

selection these capabilities will allow

custom step authors build more powerful

custom

steps that was Sasha karpinsky Joe

Madden and Alex vaan with this month's

rundown I know that the Microsoft Word

integration is something a lot of users

are excited about because it's such a

great way to share insights from svia

thanks to the three experts for coming

back and being regulars on our show now

it's time for our interview with Mary

Osborne about generative AI models I had

a chance to talk with Mary last week so

let's take a

look hey Mary thanks for uh returning to

the show for part two of this interview

happy to be

here yeah we're glad to have you um I

was actually out for last show so I

missed the interview entirely could you

give us a a recap on what you talked

about sure uh we recently released a

Bert based classifier for in SAS via

Bert is bidirectional encoder

representations for Transformers and it

is a large language model so it's a nice

addition to the rules based approaches

that we currently have in SAS visual

texting

analytics that's awesome so we're

hearing so much about generative AI in

terms of chat GPT could you tell me a

little bit more about what generative AI

is sure um in many cases we look at

generative AI as technology that as a

name would imply generat some sort of

content so that could be images like we

see in computer vision it could be text

like we do in my domain which is natural

language processing it could also be

tabular data or data that is more

structured that we see um much more on

the traditional structured machine

learning

standpoint very interesting so what are

some examples of how generative AI can

impact everyday

life there are actually quite a few um

one of the one of my favorites is

actually sort of the idea of teacher in

the Box um I have children and education

is something that is always top of mind

and there are some conversations

happening in the market around being

able to provide more personalized

instruction um through the use of

generative AI so having that teacher in

a box essentially for tutoring some

additional help for uh homework those

types of things and there's also the

idea of more personalized um not really

marketing um because people don't really

love to be marketed to um but there's

the idea of um instead of going out to a

website like Amazon for example and

looking at reviews which we know

sometimes people are paid to do reviews

so it can be kind of difficult to

determine whether or not that five-star

rating and that glowing review really is

accurate being able to curate

information about products so I could go

out and say you know I want to know what

the top

10 ski ski coats are uh for this season

and can you give me a list of those and

tell me what the pros and cons are so

instead of going to one specific vendor

being able to see more of a blanket

approach um more of a general approach

to marketing um and through through the

use of things like curated

lists lot of cool examples there I like

the the ski coat example you gave there

are some some business uses that you're

excited

about there um one of my favorite

examples I actually learned um this

weekend at a conference is being able to

use things like synthetic data

generation so at SAS we don't only do

generative AI through large language

models in the text side but we also do

play in the generative AI space on the

structured tabular side so if you are uh

a researcher and you're researching rare

diseases and maybe you only have a

population of a thousand people with

that rare disease it can be really hard

to model that kind of data so in in many

cases we need to expand the data

traditional approaches of expanding data

random random approaches um traditional

statistical I methods don't typically

work as well as we would like um by

introducing the idea of neural networks

and uh generative adversarial models we

have the ability to generate tabular

data that is similar to the original

data so it gives us much a much better

foundation for additional modeling and I

think that's a really interesting use

case um there's also of course all of

the content related uh use cases on the

large language model side so anything we

can do to speed up uh curation of

information um take advantage of the

technology to knock out some of the more

mundane aspects of content creation I

think are

benefits yeah that's super interesting

uh generative AI in general sounds super

complex though could you talk about some

of the key technology that is being uh

played here sure I mentioned large

language models and that we're

supporting one right now in Bert um

large language models that do generative

AI are are predicated on the idea of a

transformer based architecture um that

was a

groundbreaking

uh development that's the right word

groundbreaking development uh in in

terms of modeling and natural language

processing and it gave us a really good

way to not only um do basic things like

classification summarization those types

of things that we often think about in

terms of natural language processing but

it also moved the needle further and

being able to truly generate novel

content uh so you'll hear things about

Transformer models not the Optimus Prime

more than meets the eye variety but in

terms of large language models you'll

also uh hear things like ber GPT um all

of those are fall into the realm of the

large language model but on the

structured side we also support

generative adversarial models for

generating tabular synthetic data as

well as smot

very cool so what does the future of all

of this look like in terms of natural

language processing at SAS and and

Beyond what does that look

like this is something that we've put a

lot of thought into and we're going to

continue to put a lot of thought into

there are a lot of concerns around

generative AI um there are a lot of pros

and cons and at SAS we've always focused

on developing models that people can

trust and methods that people can trust

so rather than jumping Allin

uh to novel generation of text which

we've seen in many use cases uh in the

media about model hallucinations where

the models go off and say things that

aren't necessarily true but they say it

in a way that's believable we want to

make sure that we mitigate those types

of risks before we make technology

available from SAS in that domain so

we're taking a conservative approach um

doing a lot of research we have uh folks

in our R&D area prototyping a variety of

different things um that we hope to

bring to Market but we always want to

keep keep an eye on the feasibility

these models are really expensive to run

um they take a lot of compute power so

we want to make sure that whatever we

generate whatever we make available to

our customers really does um give an RO

a strong Roi because if you're going to

pay for them you want to make sure

you're getting something tangible in

return um we also want to make sure that

we're following an ethical path and

there are a lot of discussions around

ethics when it comes to generative Ai

and AI in general uh where does the data

come from in order to train these large

language models you have to have a

tremendous amount of data and there are

a lot of concerns about the way some of

the um off the-shelf right now uh large

language models are are built and

pre-trained using Wikipedia and other

internet-based sources who owns that

data and are there privacy concerns we

want to make sure that we're following

the most ethical path forward so as far

as generative AI at SAS uh we're always

going to be um keeping an eye on the

ethical side and we want to make sure

that whatever we produce is done um with

an eye on mitigating harm we don't we

definitely don't want to introduce harm

uh and doing things in a way that's

really going to bring True Value to our

customers and reduce risk because

they're all there are a lot of risks uh

with generative

AI for sure lots of points of concern

there about the ethics and cost to

compute but a lot to look forward to as

well um I've been seeing more and more

more jat uh chat GPT versions come out

like one month to the next the growth on

that is pretty exponential could you

talk a little bit about the rate of that

growth and what the newest version

offers and the the the timeline for that

sure um chat GPT is really fun I mean I

think everybody has everybody who's

involved in technology has played with

it in one form or another um I have

friends who have their grandparents Now

using chat GPT to generate recipes for

Sunday dinners uh so we have our

grandmothers and our great-grandmothers

involved uh in technology which is

really really cool not all the recipes

are really good so you have to take that

for whatever whatever it's worth um but

these models are here to stay so they're

not going to go away they're going to

continue to improve and as open AI has

has released um additional versions so

chy GPT the original was at running at

GPT 3.5 and they have released GP 4 and

that model is showing even more promise

so the the research in this area is

amazing there's there's a lot of work

being put into it by uh by so many uh

people organizations because there's a

lot of interest in it um so it's

technology that's here to stay it's not

going away uh it's going to continue to

improve and as long as we keep an eye on

it from an ethical

standpoint I say the sky's the

limit yeah it's a lot of exciting stuff

you'd have to be a really Brave one to

try a chat GPT recipe for now some of

the recipes are a little questionable

yeah a little sketchy but I'm sure it'll

get to the point where it'll be just

chef's kiss type of stuff hopefully um

question for you uh for the Casual chat

GPT generative AI user what are some

some things that they should watch out

for just general you know Pro tips best

practices that we should keep in mind

I think my favorite because like said

we're all engaging with chat GPT there

are people that are using it to generate

a tremendous amount of content be

careful um there is risk uh the the

models do generate really excellent

sounding results and sometimes they're

not really that trustworthy uh my

favorite example recently I asked chat

GPT about text to speech synthesis which

is an area that I find interesting which

is essentially um having a machine speak

like a human so you have to think about

things like inflection and tone and the

rise and fall of the voice depending on

what you're talking about so I thought

it'd be interesting to see what it came

back with and it gave me a really nice

explanation I asked it deci it sources

and it came back with four papers and

they all sounded believable and it gave

me links to the papers which I thought

though that's really cool so I click on

the links I get 404 not found errors so

the links are bad so I thought okay well

I'm just going to search for the papers

the papers don't actually exist so they

sound plausible but they're not real

papers they were not published works so

I asked chat GPT where can I find this

type of information on the ACL Anthology

which is a really big repository of

papers around computational Linguistics

and natural language processing so it

gave me the same paper names with ACL

Anthology links when I clicked on those

links the P it returned papers but they

weren't the papers that were cited

because those papers didn't exist uh

such took it one step further last step

um I asked it for the authors of these

invented papers and it gave me authors

that publish in the space um but the

combinations of researchers didn't match

up uh and they certainly didn't write

those papers because those papers don't

exist so if I had taken that information

at face value it probably wouldn't have

panned out very well because none of it

was real um it sounded plausible though

and the titles seemed very believable

the authors that were generated are real

but not in the right combinations so my

advice is use it with caution so always

check you know always verify the work um

I uh I work with students and I tell

them that in general if you're going to

use generative AI to do your homework

your grade is probably going to reflect

the level of effort you put into it so

use it with

caution yes good point they're very good

at sounding convincing sounding real

sounding human when it's actually not at

all uh break the fourth wall a little

bit this Mary Osborne that we have on

right now is actually generated by chat

GPT we just threw in prompt and here she

is convincing

right Mary thank you so much for for

coming on the show again and uh we hope

to have you back for another interview

great it's been my pleasure thank

you just to clarify that was the real

Mary Osborne AI isn't at that level yet

abilities across the entire analytics

life cycle including preparing data

creating and viewing reports building

models automating model deployment and

visualizing event streams so try it out

today go to sas.com slva for complete

information and to sign up for the trial

well we reached the end of this month's

show but we'll be back at it again next

month with more SAS via features and

updates if you're watching this on

YouTube why not give us a like And

subscribe to the SAS users YouTube

channel click on that Bell so you'll get

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live for our next show in May until then

comment on what topics you'd like to see

in future shows I've been your host

Thiago Doza that was the word for

today's updates and I'll see you next

time thanks

[Music]

everyone