Interview w Marios Michailidis | What does it take to become #1 on Kaggle | DSB 2019, 14th Pos Sol

Sanyam Bhutani: Hey, this is
Sanyam Bhutani and you’re listening to “Chai Time Data
Science”, a podcast for data science enthusiasts, where I
interview practitioners, researchers, and Kagglers about
their journey, experience, and talk all things about data
science. Hello, and welcome to another
episode of the “Chai Time Data Science” show where I interview
Kaggle legend double Kaggle Grand Master, Mariosfor the
second time on my interview series. If you haven’t yet, read
the previous blog interview. Please do check it out because
this interview really takes off where the previous one had
ended. In this interview, we continue talking a lot about
Kaggle and we talked about how Kaggle has helped Marios in his
data science journey, and his data science work at, all
about the projects where he is contributing to. We also discuss
his recent gold winning solution to the data science poll 2019
competition. And I believe the approaches shared by Marios are
applied, even generally, outside of that competition. I feel we
also touch upon a very important topic that isn’t discussed as
much on this podcast, the personal side of things, and the
personal sacrifices it takes to really become the best become
the best in the world become the best on Kaggle, like the
sacrifices it took Marios to become number one on Kaggle both
in competitions and discussions. So please stay tuned for that
discussion towards the later end of this interview. For now,
here’s my interview with Kaggle legend, Grand Master
Kazanova-Marios, please enjoy this. Hi everyone. It’s a huge
privilege for me to be talking to Kaggle legend for the second
time Grand Master Marios, Marios, thank you so much for
joining me again on the interview series. Marios: Now, thank you for
inviting me in this, you know, very popular series. And by the
way, you have done a great work on, you know, interview,
interviewing many different people in this field. Very
nicely structured, really privileged to be invited. Thank
you. Sanyam Bhutani: I’m really lucky
that great people like you keep saying yes and I keep getting
lucky. I have, I’m lucky keep continue asking stupid questions
to really smart people. Marios: No your questions,
that’s really good, actually, yeah, your questions makes us
look smart. Sanyam Bhutani: Okay, so I want
to start by talking about your Kaggle and data science journey.
At what point did you really become interested in data
science to an extent that you decided to take up a career in
this? Marios: Yeah, you know, I kind
of this is this is a story I’ve told quite a few times. So maybe
I’ll put a little spin into it. I’ll give a little bit more more
context. I was I’m originally from Greece. So I think it was
around 2010 that I finished university and that was in my
family had a history with accounting. So I was either
going to do this also in Greece, we started having some economic
recession back then. So, um, I didn’t really want to do
accounting, so I decided to do masters and I went to the UK to
do risk management. And I was really lucky because the course
was very good. But it wasn’t really focused on on predictive
modeling that at the end of the year, they had University was
organizing some talks, you know, enterpreneurship type of talks
where different business professionals were coming and
they were trying to inspire you about what to do next is
something that the university does really well. And there was
a guy who came, who told us the story of how he made money by a
competing to the horse races. He figures the same course with us,
his name escapes me. But he took a very analytics and strategic
approach on this. He started collecting data on a daily basis
for two years he was going to the horse races without really
betting maybe he was betting just for fun, but he was mostly
gathering and collecting data. And after two years, he built a
logistic regression model in order to predict the winner and
along with some strategy, he started making money. The way he
described this, the approach he took and how he made sort of a
living out of this I was I was really impressed. It seemed like
a superpower at that point, you know, the power to gain insight
and predict anticipate the future. I think I was really
impressed. So this is really what got me into into the field.
I started playing a little bit myself. I even created a tool
called Kazanova. In Java, you know, anything I was learning, I
was putting it into a software and then I made it available
because, you know, I didn’t want that knowledge to go to waste.
And after this Kaggle came into the picture, I remember it was
when I joined my previous company was called []. They have
already hosted two Kaggle competitions by the by the time
I joined. So I remember when I joined that company, everybody
was talking about it. And they were talking about the learnings
they got from the competition. And that was an extra incentive
for me. Then I said, everybody’s talking about it, as I said,
give it a salt. And I am. So basically this is how Kaggle,
Kaggle started. Sanyam Bhutani: Okay, for the
audience, I’d like to mention in respect of Marios’ team, we’re
not talking about the open source world, which we’ve
already talked a lot about in the previous interview. Go check
that out, please, if you’re interested. But coming to Kaggle
can you tell us how has your approach now evolved over these
years that you’ve been active in? How has your approach and
also views on Kaggle evolved over all of these years? Marios: Um, my views on Kaggle
hasn’t changed a lot. Um, is a great is a great platform to to
learn many aspects from, from data science, and is a great is
a great platform to collaborate and meet new people in this
field. I guess the reason I first joined was because I
wanted to emit originally I wanted to test how good I was.
Because for for a long time you’re, you know, in your own
cell. I was learning things, but I didn’t really know how they
would fare against in a in a competitive context. And this is
what originally drove me into it. And I can say I was very
satisfied. Like, look, I mean, the first attempts were not very
good. And I think this is something everybody should
realize. I mean, I have seen a few really talented people that
they’ve done really well from the beginning. I guess I was not
one of them. Did a bit too bad either, but, you know, it takes
some time obviously to get familiar and learn the tricks.
But yeah, the what actually, after that original stage of
trying to see where I was, then I want to see how to to improve.
And I think this is where Kaggle is, is really good because it
has created the platform where it’s easy for people to share,
it is it incentivizes people to share you know, through kernels
through discussions. Iytnow, these things have been to some
extent gamified because the you can get awards from doing this. Unknown: Yeah. Marios: But I have to say that
this was happening even even before they have been, you know,
the these rock. So, generally the data science community is a
great thing. Community is community that shares, it is one
of the most generous communities I’ve seen in the whole online
spectrum. And yeah, I was learning a lot and they still
do, is a great place to to follow if you want to be up to
date with recent advances in data science. Sanyam Bhutani: Yeah. Marios: Is a great place to test
a process and benchmark yourself is a great way to collaborate is
a great way to start a project when you don’t have your own
resources. Kaggle has done a great job on this it’s it’s it’s
really amazing that they have done that that no you can
actually work without having your own resources and not only
work but only also use like GPUs for example recently. Sanyam Bhutani: Yeah. Marios: They have done an
amazing work on that. Um, so yeah, I mean, I guess doing
really well on on, on on Kaggle get it to number one, that was
just a bonus. You know, he was just he, I mean, I’m really glad
that it happened and always left with a lot of effort into it.
And even gaining some cast as well. But this, especially the
last part, they were not that important. What really drove me
was to learn to improve in my craft, you know, meet other
people in this space. And these are mainly the reasons that I
still, I keep doing it. Sanyam Bhutani: I think there’s
this one very important aspect people miss out on the fact they
feel like hey, I’m not from the United States. I’m not from San
Francisco. I don’t have access to the knowledge the people but
in your case, you you come from Greece, but he was able to
gather knowledge online just using Kaggle without I believe
having a San Francisco like or a bay area like cultures. Marios: Correct. To be fair, I
did invest time so when I enter Kaggle I didn’t start from from
zero, I would say was in a way a data scientist before data
science began exactly I think, I mean, we’ve got already got the
name of data science because I really invested on both
programming for that purpose, because I didn’t know
programming pick this up in the university by download via
buying basically some Java books. And the, so I picked up
programming for the sole purpose of trying to do work in in
analytics essentially do specific work in in predictive
modeling. So I had a very focused knowledge for that
space. And then specifically, I was trying to learn, you know,
the maths, stats and everything related to that field and yeah,
so when I entered Kaggle I sort of had that, that background.
So, but yeah, I had to pick it up from scratch. And it I think
it was tougher in a way back then because that information
was not so freely available as it is today, and they were not
courses back then, that you have in Coursera where you can pick
up a load of skills right now. Sanyam Bhutani: Yeah. Marios: Yeah, but but you know,
having said that, I’m not saying that. It’s, I guess what I’m
trying to say is that you can still you can still learn, if
you put the time, a lot of online resources, you can
actually learn a lot from Kaggle as well. If you pick up those
know that type of, of competitions. And then you just
follow the trend that a lot of people that have just learned by
doing standard competitions on Kaggle. And they obviously
didn’t do very well in the beginning but progressively they
starting doing better. Sanyam Bhutani: So can you tell
us of some skills that I believe you took away from Kaggle that
you were able to apply to the real world? And this question
what is needed some the decent debate that people are trying to
spark that hey Kaggle is not equal to real world data
science. I know most Kagglers disagree to that, and the people
were making that argument that aren’t even Kagglers, what’s
your take on that debate? Marios: I think there is some
small truth in it. And I don’t I don’t want to either mine,
Kaggle. I think it’s fair to say that it isn’t, no, I mean,
there, there are some other skills you also need in the
business environment as well. But there are certain things
where you can take from Kaggle and are very transferable and
applicable to your current work like things like how to avoid
overfeeding, I know that people are bashing Kaggle on that,
because there have been cases of probing and that’s true, there
have been cases where there hasn’t been a great work on
trying to make certain that you cannot leak information from the
test data and use that to improve performance or your
training data. But there have been many other cases where they
have done that well, and they have been like huge sifts
between public this data and private this data. Um, so yeah,
building strong validation strategies, picking up a new
techniques with actual usable code and filter out what doesn’t
work because there are a lot of papers that come out and they
claim the every month you have, every day almost you have it
comes out and beats everything else, but to be honest, unless I
see it on Kaggle now I don’t believe it you know, I said with
actionable with with actual code. Sanyam Bhutani: Yeah Marios: Right. This is where I
will believe it because I know it is tested in a very
competitive context and under a specific metric given some
constraints and so, in these are very, I mean, how to make
presentations to some extent, how to interrogate the data that
has been great work for with presenting especially since
Kaggle incorporated notebooks in general data science flow, and
at least in the context of having like a data set, which is
kind of well defined. I think these are these are great skills
and, and, and as of late also how to constrain resources,
especially with character restrictions how to be more
efficient how to make certain you get the best possible score
given some time constraints and hardware limitations and
computing limitations in general um so yeah in obviously picking
up new languages I for example I wasn’t using much are in the
still don’t use but I picked up a lot of things from from Kaggle
and even Python with something I mostly learn from Kaggle not the
actual syntax that the what’s specifically related to the data
science packages that people use on a day to day basis. Um. Yeah,
I mean, these are all these are these are skills have definitely
picked up from Kaggle and are transferable There’s some other
things where I think you still get them mostly from your
working environment. Sanyam Bhutani: Yeah. Marios: Like, how to make good
presentations, PowerPoint, how to. And I think that’s one of
the most important things how to translate a business question or
a business problem into a machine learning problem. Yeah,
it definitely, man. It’s that like, what resources will they
need? What sources? What sort of data will I need? How should
they create the validation and that strategy? Which metric to
use, then how should I test these when it goes live? How for
example, am I going to do the A B testing and see if there is an
applet, for example, how to make this scalable, how do make
certain you know, it’s it doesn’t exceed certain hardware
limitations, I guess now Kaggle has also elements that can help
you with this. But generally, it’s this aspect of the work
which still needs experience. As problems in the business
environment are not well defined. You have to define them
you have to make them well defined. Sanyam Bhutani: Like you said,
to be fair, Kaggle gives you a great head start after with the
things that to be fair, Kaggle doesn’t teach you can only learn
through the real world to which Kaggle will help you get to. Marios: Yeah, but the thing is,
you will pick up the skills eventually, absent people making
a great transition from Kaggle with Kaggle being the only
actual real data science experience to the business
world. And obviously, they take some some time for them because
they will have to learn the business part. But I’ve seen
people who have been very successful doing this and they
have seen people with a you know, they were coming from very
diverse backgrounds into Kaggle to learn data science and then
making it a successful transition to that field. Sanyam Bhutani: Okay, now; Marios: You need to give them
credit for that. I mean, 100% Kaggle definitely helps you. So
I think people are really unfair to diversity. Yeah, really very,
very unfair. Sanyam Bhutani: Coming to your
current job, you’re a data scientist at Can you
tell us what tasks are you working on? And how is your
Kaggle experience helping you currently? Marios: Yes, I’m in. You know,
h2o has a very inspiring, inspiring vision to democratize
AI. And, you know, I follow that vision. And that mostly happens
for me through this automated machine learning tool that h2o
is developing called driverless AI which automates certain parts
of the the machine learning process specifically for
supervised learning tasks. And I see myself a little bit as jack
of all trades. So I specifically contribute to the code base of
the software. I guess the part which are mostly involved is the
time series components, the automated time series components
of driverless AI, and however I’m involved in, in other parts
of the software as well. You could say I do product
management to some extent. And but at the same time, I try to
be close to the customer on both the trial face and after they
have become customers because it is really important to collect
that feedback and translate that into a tool which is not just
you know, predictive Yeah, but there’s also very useful to the
customer. Okay. And I’ve been also trying to make certain
that, you know, customers make good use of the software. But I
think this is this is really key, you know, you want to be
able to build something that is not just predictive, it’s not
just good data science, but there’s also it all the
customers will love it and will find it useful and they can
integrate it easily into the day to day processes. Kaggle really
helps me on that, because as we thrive, thrive and try to be as
productive as we can. We need to be able to see what you know,
the new things that come out, and Kaggle is a great way to do
that. I mean, you know, like, things like you know, BERT comes
out so you do Kaggle and we try to incorporate because we know
it works. And we can see the implementation different
implementations for it. And, or just, you know, another
technique comes out. And, you know, you want to be able to
take that and make certain you have the most cutting edge, the
most predictive tools, you know, to get the best possible
results. Sanyam Bhutani: I guess, to give
an example, EfficientNets that will really famous research that
came out by Google haven’t appeared on Kaggle at all, and
people know that they might not be the best networks or
architectures that you could use. Marios: Yeah, yeah.KSo yeah,
it’s also good to scout for efficient implementations week
implementations as well. And it is a lot of it on on kaggle. So
yeah, it’s, it’s really being able to extract that, that no
lids extracted the insights and the implementation themselves
and making certain you know what it is the most competitive thing
performance wise out there and then find also ways to
incorporate that. Sanyam Bhutani: You could you
mentioned for the audience can you help us understand you
mentioned you also work with customers after the transition
with the product has been made, isn’t automail supposed to be a
one click solve everything solution, why do you need to
work with customers after they have driverless AI? Marios: Yeah, no, I mean, you
know, there are a lot of things that can arise you know, when
you put the model into production, you know, how do you
track performance when you need to rescore or retrain your
model, you know thrifts when your model stops performing or
how do you know sometimes you may also help on exactly
translating the business problem into that problem that in this
case driverless AI can solve this. And yeah, so it’s
definitely not just, it’s not just one, one click. So,
problems need to be formatted, you know, in certain ways, you
know, the big the right metrics, you need to have some good test
strategies as well, you know, to be able to monitor that the
model is doing well. And, and then obviously, you have to
integrate the code that comes out from the tools, the
different coding artifacts, and then incorporate that into the
pipelines that you know, the customer has. And all of this,
you know, takes quite some, it takes some work, you know. So,
yeah, you need to you need to, to follow up on that sometimes,
you know, to help making certain that you know, the the pipeline
is as best as possible. Sanyam Bhutani: AI is not
replacing it anytime soon. Marios: Hehe, no no. Sanyam Bhutani: Do you think
someone can achieve a silver or even gold medal just using
driveless AI, is that a possibility? Marios: We can definitely
achieve it on on some of the past competitions. I think
what’s a little bit hard now is that you cannot really control
the external data that everybody’s using. I think some
of the old competitions which were more constraints constraint
which you knew that you know, this is the data said no other
external sources. For example, like there was this this BNP
Paribas hosted competition with with claims. With we can do
really well like we can get very close to adopt them within few
hours of driverless AI working. Um, and that’s because this was
a competition that was you know, it’s there were no really licks,
you know, there were not external data sources where
people could bring in and everybody could bring in
different things. So if you have such a constrained environment,
I think, yeah, it should be able to. Okay. Sanyam Bhutani: Speaking of
medals, I want to discuss your latest a gold medal from the
Data Science Bowl competition 2019 one for the audience,
although it ended 2020 but can you before we talk about your
solution, can you help us set the stage about the problem
statement and the challenges so to speak. Marios: The problem statement
was so you have different kids that they were using an app.
Where in this app? They could play different games or I think
what’s different educational I think type of videos. And then
they were, and at some point, they will have to do some
assessment tests. So some assess them games. And the, essentially
what you were tasked to predict is how they will do in that
test. So given the learning experience that the person had
using the app, can we see can we predict how they are going to do
on that assessment test? I think that was the main statement they
wanted. In other words, the modeling statement, I guess,
thinks that the, the host wanted to derive were probably a lot
about inside and whether that educational price prior to the
assessment, whether it does really help on driving a better
assessment, and the thing it does, because first of all, I
will have to credit my team and that competition, they did a
great job on creating diverse solutions. What I can say from
my part is I approach this a lot as a time series problem. So I
kept at this I measure the the historical records of each one
of these kids in in different ways or users anyway. Like
everything they have done, where they’ve clicked wherever they
have misclicked how well they have done in previous
assessments, in words, assessments, they’ve done well
or not so well. How much time they were taking between
assessments, how many pauses and something else idea is that I
quickly realized that the test data had a different
distribution in many of these features in regards to the
training data because generally the test data you had last
history than the training data so you had assessments which
were earlier in the process while in the training data, you
had some assessments which were quite late in the process or the
person had various assessments and had played for many, many
more hours. So I tried to put some effort to try and replicate
that within my training data. I think that was key to getting to
think very close to solo gold medals in the in the public.
Part of the of the competition um, I basically tried to trim
out no lids and historical records. And I did that multiple
times creating multiple different data sets, you can see
that as a form of bagging, or randomized auditing of different
models built on different views of historical records for each
person. And, and so my strategy was a lot based on that sort of
trying to get a little bit closer to the distribution of
the test data in regards to how much history I have available
for each child before making predictions. I have to say,
though, that in the private data, it didn’t work as well. So
it worked better on the public data than in the private data. Sanyam Bhutani: Okay. Could you
could you give us a higher level overview of your approach maybe
of this competition as a proxy to how you approach all other
competitions? How does your pipeline look like? Marios: Yeah, I think in
general, the first thing that I do is I try to, first of all
understand the problem statement. And if I can that
really sells because if you can also put some domain knowledge
into this, if you have I think that normally can help, then
understanding the metric that I’m being tested on. And are
there for example, specific techniques that optimize for
that? Do I need to build something that optimizes for
that? So these are these are questions that comes fairly
quickly into the process. And then I try to create a strategy.
After doing some some quick combiners some some
visualizations between the training and the test, the judge
will come up with a validation strategy that can help you can
help me achieve the best score in the test data. I’d like to
see how the train data differs from the test data. And what
will be the most sensible uprights to create a validation
framework that could best reflect, you know, what the
train desk setup look like? And the for example, are there
temporal elements in the data? Do I need to take a time series
or a game series approach on how I create my validation strategy?
Are there specific groups in the data, like, have different
customers in the training, they need to test data? So I have, I
need to create a validation strategy that also adheres to
that I always validate onnew customers. Um, so these are
things that come into my mind again, fairly quickly, to come
up with a strategy then what they do is they normally test
these ideas of different validation strategies, making
some really simple models and just to see which one behaves
better on on, you know, the public part of the test data. So
I use this strategy and see a next percentage of improvement.
If I include that Z feature, do I see this kind of uplift in the
test data? Which approach seems to be a bit closer, you know, I
do this kind of comparison. So I actually waste waste within
quotes, sometime in the beginning of competitions,
testing these kinds of ideas before I actually get into it. Sanyam Bhutani: Okay Marios: Um, and then, after,
after this stage, I think I’m good to go. Then I started doing
more exhausted feature engineering. I play some
automated things, obviously. But they also try to put some domain
knowledge if I have as well into it. I am obviously try and build
many different models. I will set up also a an assemble
strategy. Now with Kaggle kernels I think I’m become more
popular, I also try to do whatever I build I build it in a
way that it will be easily transferable to a Kaggle kernel.
So, separate my training and and my inference and build anything
in a way so that I can easily upload the data set or in a
trained model on on Kaggle in order to do the scoring. And
progressively I keep looking at kernels, I keep looking at what
people use and post and I try to incorporate that although I have
to say I don’t do that immediately for various reasons.
I don’t want to get affected too much but what the others are
doing. I want to be able to create because I want to
leverage the assemble. Sanyam Bhutani: Yeah. Marios: You know, put across, a
strategy, you know, data solutions can give better
results. Sanyam Bhutani: Of
experimentation. Marios: Correct. And I know that
various ideas can can get a better result. So I tried to
think of it myself in the beginning without getting
affected by what the others are doing. And then after I get
stuck, man, I’m started getting ideas from, from the forums,
and, you know, discussions and kernels. And this, this has
helped me because I’m also able to create some novel ideas in
the beginning, which I can use to my advantage when I also
incorporate what other people are doing into this pipeline. Sanyam Bhutani: Awesome. Yeah.
Now for the audience. The complete solution might be very
technical, so please find the link to the complete write up in
the description. But if you could give us a very high level
overview of the final gold winning solution to close to
this competition. Marios: In that specific one, I
don’t think I have actually. I mean, I have just a general I
think we have a general, write up of, but it’s, it’s obviously
combining different strategies which can be tricky in within
kernels. And in my case, my solution was actually about 10
to 15 minutes, and still included and assemble of eight,
nine different models, not to me, or all of them were trained
on the same data. But you know, this this data where, you know,
three versions of the original data look, and then, after I’ve
built these models, and I’d be called these model objects, I
just incorporated starting approach. And I had a mega
model, which was also be called that use these models as inputs
in order to make the final predictions for my part. Then
obviously the other people created their own solutions. I
think they tried to address the the fact that the training and
test data were different in different ways. For example,
they use different weighting or cases that can counter for
example, an assessment without having much history before that.
But generally, we all provided hold out predictions, and
obviously final predictions, and then we’ll try different
assemble strategies somewhere based on studying somewhat based
on voting. Um, and I think the one that works best was the
voting one. And that was pretty much it. Sanyam Bhutani: Okay. Marios: Yeah. Sanyam Bhutani: Can you also
tell us you’re teaming up strategy? What strategy do you
apply while teaming up with anyone on Kaggle? Marios: Yeah, I guess this has
changed. Maybe this has changed over time. In there can be I
think, various goals. I think if you’re really targeting to go
for, like the best possible results and you want to think
strategically about this, you want to team up with people who
are likely to take a different approach than you so they, for
example, the excel at different techniques, maybe someone is
very good at deep learning while someone else is very good at, I
don’t know, tuning lightGBM models, you know, and you want
to be able to form a team of people who are likely to take
diverse solutions. So that is a strategy. I think we’d all want
to be with someone who is much better than you or has much more
experience than you. At the same time, I don’t think you want to
maybe team up someone who has very little experience, this may
make it a bit more difficult. Mistakes might arise as well,
you need to make certain that you know, that person knows the
rules well, and sometimes it don’t, you know, can lead to
unfortunate events. And so, I think, yeah, as far as strategy
goes, I think that’s in in a key that you should look at that. On
the other hand, you know, you should also have like fun. So
teaming up with friends is also always a nice option, especially
after you’ve played with some people a couple of times you do
and you develop a relationship then you know it’s really nice
to play with friends. Please play with people that you can
learn from. Yeah, the this is my my my view how I say this. Sanyam Bhutani: It’s really
interesting. You mentioned the word play can you tell us even
after becoming the best the best worldwide the best on the
world’s home to data science platform, why do you continue to
Kaggle even still even today? Marios: Yeah, I don’t think I
ever I was like the best. Yeah, so the I did manage to get at
the top at some point after obviously putting a lot of hours
into it. And I guess the yu know, I still like to do this
exactly for for the same reasons I’ve mentioned before you know,
the the as I said, getting to the top was a bonus. I mean, I
did try hard to get there. It wasn’t my original goal after I
got myself into a within striking distance. I actually
thought that since I got here, I should really try and get to
the, to the top spot. But that was, you know, the fact that I
actually got there was really a bonus. The reason I did it was
because of the learning was because, you know, I wanted to
become better in my craft. I didn’t like that environment. I
did see it a little bit like a game. You know, like, you know,
you have a game. You have a leader board, you have certain
tools and actions and skills that you can put in place in
order to achieve a better score and that’s also what kept
motivating me and I put a lot of time into it as well. I guess my
approach has changed a little bit over the years because you
know obviously it takes a lot of sacrifices to be able to get a
good results consistently, I’m yeah I cannot obviously put I
don’t want to put that much time anymore. I still you know, I
want to obviously enjoy and take away the benefits I can without
killing myself in the process. Um but yeah, it’s I guess, I use
that I want it is it has an element of of play. It is. I
still say its a bit like a game. Sanyam Bhutani: I also want to
discuss another theme another message that I’d like to get
across and you’ve been very vocal about this, even on this
interview otherwise about the sacrifices, personally and even
you’re doing so much efforts on Kaggle I’m sure it took a lot of
genius and efforts both combined, it affected your
health also, how has your view been a viewpoint now changed,
because everyone gets competitive. I’m sure there’s an
element of passion, where should they draw the line? Marios: Yeah. And I have to say
that personally, I don’t regret it. So it’s, it was a conscious
decision. I knew I would in order to get to the top spot. I
will have to make sacrifices, this man working extreme amount
of hours. I remember I was working 60 hours about six hours
per week on top of my job Sanyam Bhutani: Hehehe. Marios: So I was not sleeping
much. Had to drink a lot of coffee. I was eating a lot. I
was almost never quite given even walking, running. I mean, I
mean my physique went went quite bad. Um, and that meant, you
know, you don’t go out with friends on Friday, Sunday or
Saturday nights. I’m eating a lot of sweets, it you know, it
does, it does make a lot of difference. You know, eating
sugar and chocolate, had gained maybe 20 kilos on top of my
standard weight while I was doing that, and it took us a lot
of effort to lose that. Um, but yeah, as I said, I don’t I don’t
regret it because I have learned a lot from it. I wouldn’t have
been able, I didn’t, I wasn’t as talented as others may be to
achieve this. With less time. I think I had to put the time in
it. And I don’t regret it. But I’m not going to do this again.
I think you know, I did it I mean if I if I’m able to still
get somehow good results in, in within a limited time compared
to what I was putting before um then I’m I’m I’m quite happy
with that while I still you know enjoy the process I still learn,
still pick up new skills. This is this is my views. This is my
sort of how I say this, you know how bad that is, I guess. Having
said I don’t regret it. I don’t want to encourage people to go
in and do that. I mean that I think that wouldn’t be
responsible of me. On the other hand, it’s quite a tough to say
not because I didn’t for myself. I really want to do to achieve
the top squad and but, you know, I think striking and balance is
really really important at the end of the day, it doesn’t
matter if you don’t get to that spot, you know, they have people
who have enjoyed Kaggle have enjoyed the benefits of Kaggle
they didn’t reach the top spots and they still you know, they
have been very, very, very successful. So, you know, you
should, you should prioritize, you know, yourself first you
know, try to enjoy the process, you know, striking a balance, as
I mentioned, very, very important. Yeah. Sanyam Bhutani: Even in terms of
reputation, so to speak, the Kaggle community recognizes each
other very well and even if you’re not in the top hundred
and you’re doing a few competitions here and there and
you share great ideas. The community knows you and they
always appreciate and recognize you. So you don’t have to
absolutely aim for that impossible sport even to have
that appreciation. Marios: That’s correct. Yes, I
am. And as I said, even doing one or two competitions, it can
be really beneficial for you. And you mentioned 100, even 1000
or even less, I’ve seen people, you know, just getting a silver
in a already bronze in a competition and they’re able to,
you know, get recognition out of it. And it helps them in in
their professional environment. For example, you don’t need to
get to, to the top spot in order to have your work recognized. Be
more you know, extract skills and make an impact, a bigger
impact in your work environment. Yeah, having said that, you
know, I also want to be honest because, you know, for me it I
really, I really wanted that but that’s that’s me, right? I don’t
know, this is how I was born. I don’t know I really wanted that
I really wanted to ace the game even even for a little while.
But you definitely don’t have to do that. Sanyam Bhutani: That’s a taste
of pure passion. Marios: Especially if it can be
hurtful. It was very, very tough to lose all this extra pounds
very tough. Sanyam Bhutani: That’s a taste
of genuine passion for the audience who are listening to
this episode. Now for the audience who feel completely
overwhelmed to even start competing or even try a Kaggle
competition, what would be your best advice? Marios: Just don’t think twice
and started thinking about it. The bigger is going to become
into your head. No one started, you know, being 100%
comfortable, you know, and it still you know, a new technique
or something comes out you know, a new, you know, very different
code base than what you’ve used before or, or you know, like
embedding new packets has a co pletely new syntax. You can st
ll feel a little bit ov rwhelmed in the beginning, yo
know, it’s what you need to do is just, you know, go ahead an
do it and it will take some ti e but you will get used to it
You as I mentioned, you can st rt with this and this applies to
it to general I mean, you and yo r working environment this as we
l. You will encounter new to ls you will encounter, yeah, ne
new software and new pa kages. And you need to de
elop that mentality of, you kn w, breaking something down an
fixing it up afterwards. It s you gone out, you should re
lly think twice to start with th knowledge type of co
petitions, and then just see wh t the others are doing. As I me
tioned, there are resources an courses out there, there’s ac
ually one course which I’m in olved as well, it’s called Ho
to Win Data Science co petitions, also build with ot
er excellent Kagglers from th Higher School of Economics fr
m last year. So there are th re are ways to to, you know, to
pick it up, you shouldn’t re lly think twice, just enter tr
, you will likely fail in the be inning. fail within context de
ends how you determine it has fa led. Because if your goal is to
learn you never really fail. An , yeah, just just just go for it
Don’t think like don’t make it bigger in your head than it al
eady is. You know, it’s it’s go na take some time. And and it st
ll does even for experienced pe ple when something new comes ou
but the good thing generally wi h programming based pr
nciples is that there is a pe iod where you make very li
tle progress in the beginning bu then it goes really fast un
il you usually know that when wh n you feel that you want to gi
e up because you read and you do ‘t understand you put the ti
e you make little progress da by day, but after a while, it
will go really fast. You kn w, like like once once you st
rted getting it so this is so ething you should you should ha
e always in the back of your mi d. So you’re thinking of not st
rting or giving up. Sanyam Bhutani: Now I save the
most tricky question for you towards the last I know you,
you’re a gaming fan. Can you pick a name your favorite game
of all time and Xbox or ps4 or PC, which is your preferred
platform of service? Marios: Oh, oof which one should
I pick. Sanyam Bhutani: maybe two of the Marios: All time I guess old
time the Final Fantasy series looking for now it’s I played
both on PlayStation and PC I think it is tough to select
which one I would say Final F ntast 7,8 and 9 are are the
best games I’ve played have like a strong story, nice gamep
ay and stuff to choose which o e of them to be honest. Um,
the these yeah, these are my, I guess from more recent ones. I d
d like the Mas Sanyam Bhutani: Okay. Marios: Then Horizon Zero Dawn,
which is also actually very relevant to what we do on
PlayStation four, these are games I really enjoyed. I used
to do also quite some online gaming. Back in the day, I also
think done quite quite well. I used to play Dota, in defense of
the NCS back in the day. I spent a lot of time, yeah, I’m happy
that I was actually able to take that passion from gaming, and
put it into Kaggle because it also helped me in my work. And
that’s why I say if you actually see like a game, then you know,
you can make progress in especially for a period of time,
you won’t even feel tired. You’ll be able to do this for
hours and hours and hours. And don’t feel tired because it’s
like when you play again. Sanyam Bhutani: Awesome. Thank
you. Thank you so much for all of your amazing advices and all
of your contributions to the data science and Kaggle
community and for joining you on the podcast Marios. Unknown: And thank you, likewise
for the opportunity to you know to have this podcast with you Sanyam Bhutani: Thank you so
much for listening to this episode. If you enjoyed the
show, please be sure to give it a review or feel free to shoot
me a message. You can find all of the social media links in the
description. If you like the show, please subscribe and tune
in each week to “Chai Time Data Science”.

, ,

Post navigation

One thought on “Interview w Marios Michailidis | What does it take to become #1 on Kaggle | DSB 2019, 14th Pos Sol

Leave a Reply

Your email address will not be published. Required fields are marked *