A bit of AI- S02 - Ep 7

Hi everyone, and welcome to the seventh episode of season two of the Bit of AI show

In this show, we will talk with Laura about her life in AI. Hi everyone, and welcome to the Bit of AI show

My name is Henk Buhlmann, and I'm a white male with brown hair, wearing glasses

And today I'm wearing a Kansas City Developer Conference jersey, as been told

It is a dark red and it has black sleeves. And it has the words KCBC on it

Fabulous. Good description. Good description, Hank. And my name is Amy Boyd

I'm a female with blonde, wavy hair. And today I'm wearing a navy blue t-shirt

Mine's a bit plainer than Hank's, so I have a lot more to tell you about it, really

So good. So hello, everyone. And once again, welcome to the A Bit of AI show

And for everyone who is their first bit of AI show, this show is all about the story from the people behind AI

Because Amy and me in our jobs, we meet so many people from around the globe

And we realize that there are so many different skilled people involved in creating an AI solution

from pre-sales to consultancy to actually creating the deep neural networks itself

and running them in production. So in this show, we invite people to talk about what they actually do during their working day

So we just have a chat and talk about a week. This is the seventh episode of season two

and we couldn't be more excited to start talking to our guests and learn more about what they do

And as always, all the links and all the information can be found on our website

a bit of AI dot show. And you know me, I'm always calling out the logistical elements

So if you are watching live after the show, we have an a bit of AI cafe

So kind of a space where you can chat with me and Hank or you can chat with our speaker

and ask lots of questions. We share great resources and we really build that community feeling

So if you want to take part after the show today, go to aka.ms slash a bit of AI dash cafe to join us

So that's straight after the show. Perfect. So let's get started and invite Laura to the screen

Hi, Laura. Hello, everyone. Hey, it's so great to have Laura on the show with us today

when we created the idea of a bit of AI, Laura was one of the first people that we thought of

that we wanted on the show. And so we're so, so happy that you can join us in our season two

episodes. But Laura, this is not about how we've had a conversation to get you here

This is all about you. So tell us who you are and what you do in the space of AI

Okay, so yes, first thing, good morning for those that are in the morning and good afternoon

evening for those that are in different places around the world. As Amy said, so I'm Laura

da Silva, so that will be the pronunciation in Spanish, but yeah, Laura in the UK as well

So I'm wearing just a black dress today. So I'm blonde and I'm a white female

So just that you can have an idea of how I'm wearing today. So just starting to answer your question

So I am based in Spain currently. So I'm living here with my child, so my baby

is a eight months baby boy and my husband so we are working from home with the actual situations

I think that the most of us are working from home and so just regarding to what I do in my role so

I am the capability lead for ML and AI in Cognizant Microsoft Business Group so what I do is I have

an overview of ML and data science, all the projects that we are running on the team itself

so it's more or less what I'm doing at the moment. That's amazing

Thank you so much for sharing with us. So, yeah, Cognizant works very close with Microsoft technologies, but also we've known

you for many years through the community based around data science and some Microsoft technologies

technologies as well. So it's really great to hear this amazing role that you're sitting in

Now, we always ask all of our guests and all of our jobs vary daily. It's part of the tech

industry, but we're really keen. Can you describe to us, like, what does your average day look like

What are the types of tasks you have to do in your role? okay so um every day i usually start my day with a full breakfast it could be at home or it could

be outside so if we get early so maybe we go and have a full breakfast somewhere nice and we will

put a walk and after that we like to start our day just working so um once we have a work kid in

nursery then my day is more or less like at the beginning just having a look at my emails answering

those that are relevant or those that have a high priority and then maybe i review some

food requests that i have from the day before or maybe just checking some code that maybe i'm

open or implementing um also so what i do is just to start um thinking about what i'm going to

what i'm going to do in the to say in this time because we have we are working in the scrum

and methodology so we have stand-ups every day in the morning and just to see how we are doing

in the process of the project so um we can say that half of my day is more related to technical

stuff so implementing hands-on so data engineer and ml solutions and then the other half is more

related to um technical things with clients so having maybe pre-sales or having some discussions

with clients on how the police are going and maybe creating some architecture for them and sometimes also just thinking about recruitment people that we need in our team or how we build the structure of the capabilities

so that we can offer more things inside of our company. So more or less, that's my day

That's a good split. That's a good split between the technical and the sort of more softer skill side of it as well

I know, you know, it very much depends what you're looking for in a role

But for me, that kind of mix is really what I look for

So rather than just coding all day or just doing code reviews and stuff

but to actually have the conversations and the envisioning and the building of relationships with different people

as we kind of build out these different projects. That's really, really cool. How do you find kind of a little side question for you

You mentioned you work in kind of a scrum-based approach. And so you have stand-ups, you possibly have sprint planning

and stuff like that. I'm recently working on a project where I'm doing that

and I'm finding it's so useful for me to like, for us to all keep up to date with what we're working on

Do you think in a virtual world, and we're all connecting sort of online

that that kind of connection is helping you in your role? Yes, I think that definitely, I think that when you have to give estimation of the time

that you are going to take to develop something and when you have to do a commitment on what

you are going to deliver, I think that it's quite useful to have any methodology that

is supporting this. It could be, in my case, Scrum, like Agile methodology, but you can also use Kanbis

It depends on, you know, how regularly you want to give this feedback to the client

So I think that now that we are working remote, this is supporting a lot because it's difficult

to see each other, it's difficult to have meetings regularly. So maybe one thing that you have is this catch up in the morning, so then you have this time

outside where you are developing things and you are pinging people when you really need it

So I think that it's really, at least in my case, it's helping me a lot

Yeah, no, it's always good to hear the kind of how do we work together as well as what

we actually work on as projects. Yeah, exactly. It's been a long time for me I've been in a scrum project

But it always worked well, yeah. I mostly like the Kanban method because you just keep continuing, continuing, continuing

to deliver value and little chunks. But enough about me, we don't need to hear about me doing Scrum and Kanban

It is all about you today, Laura. So I'm wondering, how did you get into AI

How did you get to this point in your career? Well, so I think AI has been quite a natural journey

because when I was a kid, I was really interested in maths

and also in computer science. that time we had the first computers and I was really lucky you know that we had

one in my school and I started doing things it's really really basic things

you know like navigating directories of things like that but really excited about

this and then when I went to the university I studied maths so pure

mathematics and I did like a bachelor and master's on that and then after that

I got so excited because in the degree I was able to learn C and R. At that time I was learning R

for statistics and then I wanted to know more so I did a Masters in Computer Science as well

and that in at that time so that masters was related to in advanced computing techniques so

it is like expert systems it was a branch about algorithm it was about by inspired algorithms so

it was a different kind of algorithms and it was the first time that I was just doing something

related to machine learning so but it was quite theoretical and also I started

my PhD in computer science it was related to parallel computing and then I

was able to see different things then I moved to industry and I started with

C-share development sequel then when I moved to the UK I started doing data

science so it was a long journey but at the beginning of my journey in industry

I started doing a lot of data science POCs so for those that are doing a lot of

pieces that was my beginning as well and then I was evolving as the company was

evolving so really quickly the company needed a team lead for data science to

create a recruitment pipeline, just to go and talk to the clients with, you know, to

different customers. And then was when I started just being technically principal data scientist

Yes. And I think that somehow that was my journey just to be the capability lead today

So that makes sense. That makes total sense. Amazing. I think you're one of our first guests

that actually started in university leading up to the field of AI

We had a lot of people that switched later on, that studied like history and then moved to AI

So my other question is, it sounds amazing what you do during day

but there must be something really annoying about your role and your day

So we want to know what that is. Yeah, well, I have more than one just in my mind

Yes, I will go for one that is more like annoying, it's more like challenging

And I still see this when we have engagement with different clients and we are working

on data science projects is how separated are engineers, data scientists and business

purposes. So it's like three different worlds that have to be just together. So they have

to collaborate, but somehow because data science came later on, I think that sometimes you

can see these silos so that they are focusing, like in academia, just getting the best performance

of something, maybe their model or something like that, or just using the latest algorithm

But then the business purposes are completely different So they are disconnected Sometimes the messages are difficult so that the communication between data scientists and stakeholders sometimes it not as easy as it

should be and then the other side is engineering teams where those engineering teams they have a

lot of things to do and sometimes they don't have this dedicated time to get models into production

So there is this. So I see this because I come from both places, from the data science and engineering side, so I can understand both of them. And also I understand the business part because I know that if we are building something is for to get a profit on it or just to get something that's valuable for the business

So, as I said, it's not that it is annoying. It's like, you know, sometimes it's challenging

and I still see this today. So I hope, you know, that we can, you know, just find ways to

So for us, we are doing this for our clients. We are helping this to smooth this communication

and also to solve this problem that they are facing in their current teams

Interesting. That's so true. It is kind of like, yeah, it's kind of like when I was in engineering

I went through that whole transition of like engineering and operations that have to work together and that we didn't like put it on a floppy disk

and walk to production and then copied it on the server. So this feels kind of like the same that now data science have to

your team the data scientists have to be included in like the larger DevOps team

we're including data science and I think that also it also includes the

different life cycles so the life cycle for data science is a bit

different of the life cycle for engineering and the engineer is there

just so willing to see you know code that is really clean and ready for

production is performing well, when a data scientist is focusing and get the best performance

and you know that the prediction is quite accurate. So then you know so both of them they are trained

to do their best in their roles but at the same time just to get together you know and go for the

same purpose sometimes is a challenge. I can imagine that. That's such a good one and the reason we ask

that question always is because um ai seems like you know the sparkly unicorn of the technology

industry at the moment and uh it's really good for us to be honest that when people get into it

it's not it's not perfect yet there are things like you said you know more work needs to be done

to get people to work together more than what needs to be done on ethical ai and all of this

kind of so so uh good thank you for sharing that that's um it's really important for us very quickly

before we move on to our quickfire round just to prepare you that is coming um we wanted to briefly

talk about community because i know you originally from the inspiring women in data science community

used to run in london um and i just wanted to ask you what's your perspective on community

What have you got from it and sort of given to it over time

Yeah, so thank you for this question because community is something that I am really passionate about

So I started inspiring women in data science as it was like three or four years ago

Then I became a Microsoft MVP in AI. and the whole idea of inspiring women in data science to connect with other women and show

what's possible and also to see that it doesn't matter well so you had to want to you have to

really want to become a data scientist as that for sure because it brings it an effort so you

have to do an effort at the same time that you are learning new things so it's good that you'll

get excited about the salary because here just to be sincere but also you have to be excited about

the effort that you have to put yes to do a change so in inspiring women in data science

we wanted to inspire some women that they are in different background with different backgrounds

Some of them, they are from psychology. Some of them, they are coming from biology

Some of them, they are coming from linguistics. And I can say that all of them

they have a place in data science because it's really broad. And it's something that you can apply

for so many different things. It's really valuable, the knowledge that they have

in their different backgrounds. So the best thing that I got from the community is that after a couple of years

you're running month by month, technical and inspirational talks, we got some women that they moved to data science

Today they are data scientists, they started their masters, they stopped their work

and they said, okay, I want to become a data scientist. I got passionate about this

so i'm going to move to this uh industry so and for me it was something really big so i couldn't

imagine that we could impact in other people's lives in this way i'm really proud of all of them

you know for being so so so passionate and and do the deserve work and also just pursue what they

one so yeah so i think that that was something big oh no no i really really like that i always

say that about this show i'm like if one person comes to the show and then like you know gets

excited about working in ai and i don't know a few years down the line tells us that you know

they really enjoyed watching this and that um they got into it that that would be success like i think

that's part of it to just see that you're not kind of alone you're like other people you've all got

great different perspectives and they're super valuable and Laura you're not running Inspiring

Women in Data Science now you've been super busy over the last couple of years but people

you know do follow Laura, Laura Data Sci on Twitter find her on LinkedIn if there's anything

in the future honestly you will definitely want to get involved because it was such a great community

but Laura we do need to move on to our quick fire questions um so we have six questions I know are

you ready like are you prepared don't worry it's all good everyone loves this bit um so our quick

fire questions we have six questions me and Hank are going to alternate um and ask you the question

if you can keep it to either a word phrase or sentence uh we appreciate that um just so that we can make sure we keeping on time but also the first thing that comes to your mind

is the most important thing. Okay, so let's get started. Question number one, fairly easy one

you actually briefly mentioned it in your intro. So what was your first computer

Well, I don't know. I think that the only thing that I remember, I think that is the

operating system. Well, when I had my own computer, it was Windows NT. I think that

was the operating system at that time. So it was, yeah, but the first one, it was a really

old one and we couldn't do much with that one. I don't remember the name of the computer

no no I only because I was thinking like how would I answer this question like I have no idea

we got given our family computer by like my granddad and all I remember is a form of windows

on it that was that was it I don't like know what type of pc is but some people are very like very

much know what it is but that's great windows NT we love that that we can age mark you now

Next question. What programming language was used in the last project you worked on

So the project I'm currently working on is I'm using PySpark. That's what I use. It's

basically some Python with a spark on it. Nice. Nice bit of a mix. We're seeing a pattern

there. Our next question is an interesting one. So what is the

most useful thing you've learned in AI? You can't meet them. I just want to see the AI. I think that

yeah, don't trust when people say that the data is clean. I'm ready. So if I don't see I don't

If I don't see it, I don't believe it is the best quote, I think, of this episode so far

No, that's a really good one. A couple of people have mentioned data, actually, for that question

So nice. That's an interesting one. What's your favorite event on the AI calendar

I think that for several years it was the Open Data Science Conference

I really like that one. but then also the data and AI submit from database that one is a good one as well

very good very good we'll have to share some links for people in the cafe oh

ODSC or open data science community they they have meetups and things like that I'm signed up I get

emails from them all the time they have some amazing sessions so that's a really really good

call out. So our fifth question, so what area of AI is on your list to skill up on next

So have you got anything that you're currently wanting to learn? Well, so I have to say that data science is really broad. So sometimes it's just focus

on the project that you have and then you find the tools. Like, you know, the other way

around is sometimes difficult to get a tool and apply to the project but I would like to revisit

the things that I learned in my masters so like by when they buy inspired algorithms and things

like that and see how they are being used for AI today because I am sure that they are being used

in academia and I would like to know a bit more about that amazing that was a great uh one that

that actually wraps us up so we're gonna keep it to fudge um today but thank you so much all those

answers so some were quite similar to um what some people have said and some are quite different as

well and that's what we love about our quickfire questions they really bring out lots of different

sides of people but with that unfortunately laura that is all we have time for today how quick does

this 30 minutes go to spend with you um but don't worry um me henk laura will all be in um the cafe

the a bit of ai cafe just after the show so do join us there uh and we can continue this conversation

share links and stuff like that. So with that, thank you so much, Laura, for joining us

Thank you for having me. A pleasure always. Thank you. Thank you

Okay. So to wrap up the show in the last couple of minutes, you know what we're like

We always want to tell you about what else is happening in the AI space and the event space

So, Hank, we wanted to talk a bit about the AI show

Can you tell us a little bit more about that? Oh, yeah, absolutely. The AI show is a show that is aired on Lauren TV every Friday for me evening

for an American time zone afternoon, I guess. It's like hour 11

And this show is more about the people in AI. the AI show is all about the technical things like new products, how do you do things. It's

two hour long, lots of hands-on coding where they actually show you how to use, in this case

the PyTorch profiler. And then the Onyx runtime for web. I didn't know about that one, so I'm

definitely going to watch. And of course, you don't have to catch it live. It is always on

channel nine. So if you Google AI show Microsoft, you will find it

Yeah, no, no. Great shout out. I often do a little catch up sometimes on a Monday morning when I'm

like clearing up my inbox and stuff, have it on a second screen. And you learn so much just about

different things if you've not yet used them. And then one final call out, Microsoft Ignite

one of our flagship conferences for the year, is coming up next week, second to the fourth of

November. If you want to get to see what's available for you there, go to myignite.microsoft.com

And with that, we'll wrap up. Thank you so much for joining us today. We hope you've enjoyed the

show. Remember, you can catch us. I enjoyed this one so much at abitofai.show on our website

do come and join us in the cafe aka.ms slash a bit of ai dash cafe and with that thanks so

much for watching this has been the a bit of ai show with hank and amy