Welcome to GOTO Unscripted. We are here at the GOTO conference
in Copenhagen on the second day. And my name is
Linda Stougaard Nielsen. With me, I have Moez Ali. Hey, my name is Moez. I'm a data scientist, and
very excited to be here today talking with Linda. Right. And I think we will talk a little
bit about the concept of a citizen data scientist. Can you explain what that is?
Yes, sure. So, a citizen data scientist is a term coined by Gartner a couple of years ago,
and it is described as a person who uses or leverages predictive and prescriptive
models in their work, but their primary domain or line of work is not statistics or analytics. So,
think of it as a secondary persona to add more fuel to your data science team.
So, this could be any person who has any interest in the domain that they're working with,
basically. So, a person who has no idea of what a machine learning model even is,
or how the data is structured, but just knows about the domain, right?
Know about the domain, and obviously, he's interested to leverage no-code, low-code tools to
their advantage to solve the problem. So, imagine a doctor interested in knowing what part of an
image, or what part of, let's say it's a problem of diagnosing a condition from an x-ray image
or an MRI image. And the doctor is interested in leveraging kind of data to understand what
causes the condition. In that case, the doctor would not be a data scientist but would be using
data science and data to solve the problem. And how would he go about that? Because I mean,
nowadays, if you are working as a data scientist, right? You work directly,
you write SQL code, you write Python, and you work with new, better notebooks, or even more
advanced technologies. So, you can't expect a citizen data scientist to know all this code,
right? So, how is this typically done? So, in the last few years, there's a wave
of these tools that are called no-code and low-code tools where you don't necessarily
need to write code to be able to build a machine learning model or test a machine learning model.
You have very easy-to-use, friendly interfaces that you can use to kind of solve these problems.
Yes. And you were even working on one, right? Yes. Of course. In 2020, two years ago,
I led the creation of a project called PyCaret, which is an open source low-code, machine-learning
library in Python. You still have to code that's why we call it low-code. You still have to code,
write a few lines of code instead of writing many lines of code, but then you also have no
code tools where you completely, it's the code layer is completely abstracted. You
have beautiful front-end UIs to work with. So, these tools that you typically use as a
business analyst such as Tableau or kind of these kinds of tools.
Yes. The target audience of these tools originally was thought of as business
analysts or citizen data scientists. Citizen data scientist is not like a formal designation,
right? You can be a marketing analyst and can be a citizen of data science if you're using
data, and leveraging data to solve your problems, right? But originally, all these tools come with
their story of less technical personas using these tools. But what I've seen over time in the last
few years is even your hardcore engineering and data science resources will also sometime
use tools like this to maybe configure an ETL pipeline, compressor model, or train a
model. It's not only about technical skills, or technical acumen, it's also about these kinds
of tools that are really good, you know, time savers. They're good productivity tools as well.
Exactly. So, we are talking about this level of abstraction, right? Before this talk,
we talked about AutoML, which is another level of abstraction that has been introduced within
the last couple of years, whereas actually a machine learning code that finds the best
machine learning code or model for you. So, it's a model that finds the best model for you. So,
it's sort of an abstraction level, which is making people free to do other interesting stuff than the
low-level stuff, or well, finding a machine, a good model is not necessarily low level, but you
can get more creative by having these tools. Of course. AutoML has been there for over a
decade now. We have seen first-tier companies like Data Abort, HTO, and these products start
coming in 2012, so 10 years ago, right? Today, what we are seeing is the second-tier automation
in this space whereby you'll see that all these big companies are converting AutoML into, like,
API service where you send your data and target through an API endpoint. Everything
is abstracted. You don't see anything. It'll get processed on the cloud. And at the end of the day,
you would get the forecast or prediction back, right? Google has this product called Vertex AI,
and what they're essentially doing is they're building these forecasting engines behind the
scene for other companies to use in their product to eventually sell a product to consumers, right?
What we are seeing today is more certain automation in these products. We have seen
that all these capabilities are now integrating with the platform, whether internal or purchase
platform, rather than operating as an individual software as a service that
used to couple of years ago. So, now AutoML is a big part I think of every organization or will
be a big part of every organization. Yes, I agree with you. But also, I mean,
we are talking about abstraction, and we are talking about people would not necessarily knowing
what's going on under the hood is using these models. And then there's also this other idea that
with really powerful tools, you also have a lot of responsibilities, right? You can still build,
and do a lot of wrong things, even though you have these powerful tools because, if you
don't select your data correctly, if you don't represent your whole population, for example,
you can end up building a fantastic model based on your training data, but if that is not what your
population in real life is going to be for your app afterward, then it's not doing a good job
when you're then moving it to work in real life. So, we talked about that before. If you have an
image recognition system that is trained on the wrong data, it might go out afterward, and not be
able to perform at all in your target population, if you don't make sure that your data is correct.
So, even though you have all these tools, there are still other skill sets that we need to...
Of course. So AutoML wouldn't prevent stupidity at work, right? It's the same thing as you can
have a car with a license, but you can still go on the road and do an accident. Right? So,
the gloomy knowledge, the number one thing, AutoML won't replace that. All the underlying statistical
correctness of how you sample your data, how you prevent bias, how you collect the data,
etc. All that has to be part of your skillset or your team's skillset. So, if you go on Google now
and search for AutoML, I can bet that over 50% of the time article that mentions AutoML would also
mention job security, or data scientists losing a job because of AutoML. I don't believe in that
idea because when Microsoft Excel was invented, nobody said Excel would replace the accountant
shop, right? You still need a person. I think AutoML is one of the tools in the stack of data
science teams. It wouldn't replace or it wouldn't, you know, provide a supplement to logical acumen.
You still need to have that in your team. Yes, I absolutely agree with you.
I also don't feel threatened by its emerging, but I think it opens for much more creativity
because you can have people that actually work in the domain. They're not as far away from the
tools as they used to be. If you were writing C code, or whatever you would be writing that
the closer you get to where they can actually have insights from playing around with the data
themselves, with playing around even with models themselves, they get much more insights. And, yes,
they might require some people to help them find the right data, making sure that they're
actually looking in the right direction, the right models, the type of models that they're
applying. But at least the gap is closing, which is something that I really like because it also
gives us opportunities to do something that's more fun, instead of doing what a lot of people
have already done, finding the best model for training, for recognizing images, for example.
Yes. A lot of people have already done that, so let's just leverage on that.
Yes. Another way to look at it is I always see data sciences as two parts. There is art in that,
and there's science. I think science can be automated and is getting automated, right? The art
part stays with humans. Let me ask this, would you hire a person who always writes the implementation
of neural networks from a scratch? Or would you hire somebody who leverages, out of the
open source libraries like TensorFlow ? I bet you do second, right? Because the first person
who writes everything from a scratch every time would spend a lot of time, and you wouldn't want
that as a manager, right? So, I think AutoML is saving time, and giving you that time back
to focus on things that matter. That thing could be more engagement with domain experts,
more time spent on identifying new features, engineering new features, or even communicating,
or quantifying the value of the project, right? The idea is you do what as a human you're best at,
and let the machine do what the machine can do better than you, right? And if you think
about AutoML at a grounded level, at the end of the day, it's iteration, or a certain set
of attempts in space, right? And I'm pretty sure a machine can do iteration better than us.
Yes. I really like this way of putting it, saying that we have science, and we have art because
people have often identified our area with the nerds that you're just being a nerd. But I often
said that, well, you don't get anywhere without a lot of creativity because you really need to be
able to not just work within the box, but actually go outside of the box and explore, and you have to
be creative, you have to do the art in order to do something innovative, to do something in this
area. And if you can have... the more tools you can have to help you do the standard tasks, the
more time you have to do the actually interesting and do art. So, I like that way of putting it.
Absolutely. Also, if you think about it, like, we have been hearing this for a long time that
there would be a shortage of data scientists in the future. I think there would be a shortage of
people who can convert business problems into machine-learning problems. I don't think so
technical skills will be short because of the way we are automating and abstracting everything from
cars to software, to a washing machine. I don't think so. We are far away from a point where we'll
be surrounded by abstraction. I think the real shortage of talent is in people who look at, okay,
I am having inventory shortages again and again in my business, this is because of lack of quality
forecast. So, this is actually a forecasting problem. If I solve that, I'll be able to,
you know, overcome the inventory shortage problem. I think we have a problem here of shortage
of talent that can identify a problem, or take a business problem and frame it into a
machine-learning problem, and then collaborate and coordinate with different pieces, different teams,
and organizations to make that project happen, right? And that's exactly what I think a citizen
data scientist is, somebody who is closest to the business problem, and can convert that problem
into, a machine learning problem, at least frame it, not practically do it, frame it, quantify the
value of it, convince people that we should solve this problem, and why should solve this problem.
I think actually that would also attract much more people into this area that you
don't have to deal with all the nitty-gritty details of it, that you have this abstraction
level that you can deal with that problems, and solve the problems instead. I think that
would be a really good way to attract people. Right. Exactly. Right. I mean, if you work in
a company where you do not have motivated people, or you have not taken the business in confidence,
you can build as fancy a data science team as you want, but you won't have any results. At the end
of the day, you need to satisfy a business ask or a business problem, and you need the business to
be in confidence with you. Thanks a lot. It was
very interesting talking to you. Same here. It was a pleasure meeting you,
Linda. Thank you. Thank you.