SLICED - will you make the (data science) cut?

Just last night, Ethan Douglas, one of our contestants at the start of the season, he kind of just described himself as a data analyst and didn't have a tremendous amount of experience as a professional data scientist writing predictive models. And he shared with me he has really matured as a data scientist more in the last couple months through Sliced than he has in the last few years as a self taught coder and he considers himself now a data scientist. So it's potential embarrassment and front of a worldwide audience watching you live. Code is a very strong forcing function to learn. So I think for folks out there who are hoping to glean the same thing from Sliced, I think the message is to just put yourself out there and set some time boxed goals for yourself. And this is a great set of motions to go through to learn how to do data science end to end.

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Welcome to Practical AI, a weekly podcast that makes artificial intelligence practical, productive and accessible to everyone. This is where conversations around AI, machine learning and data science happen. Join the community and slack with us around various topics of the show@changelog.com community and follow us on Twitter. We're racticalaifm.

Well, welcome to another episode of Practical AI this is Daniel Whitenack. I am a data scientist with SIL International. I'm joined, as always, by my co host, Chris Benson, who is a tech strategist at Lockheed Martin. How are you doing, Chris?

I am doing very well today. Daniel, how are you doing?

I am doing awesome because I think my favorite new data science thing is this thing called Sliced. And today we have Nick Wan and Meg Rysdal with us from Sliced. So I am so excited. I almost feel like I'm on Slice, seeing both of your faces.

I've gotten that a few times.

Yeah, yeah, we don't have the, you know, the podcast just has the audio, but we're seeing your faces now, so welcome.

Thank you. Yeah. Super excited to be here.

Thanks for having us.

Yeah.

Well, so some of our listeners, I don't know how they could not know about Sliced, but let's just suppose that maybe some of them don't know about Sliced. Can one of you just give a little bit of an intro for what it is?

Go for it, Nick.

All right. Slice is a competitive data science game show that we have on my Twitch channel, and it's for data scientists, and they receive a data set that they've never seen before, and they have two hours to create predictive models, data visualization, find good coding practices or random things that me and Meg have deemed important to do in the data set, and all these things earn them points. And at the end of the night, depending on how well your model performed, how good your data visualization was based off me and Meg's qualitative judging, and how many of these things that we've buried in the data set that you find, which we call golden features, these all add up and the person with the most points wins.

That's awesome. So I have two follow ups. One, why is this not yet on network tv?

Great question.

And two, maybe, yeah, could you give us just a little bit of a background? I know, Meg, you work with Kaggle, so I know that there's some background in sort of competitive data science leaderboard type things. Is that part of the inspiration or how did the collaboration come about and get started?

Yeah. Where did the idea come from?

Yeah, so I think there's like a few questions to unpack there. Where the idea came from is really fun. So I'll talk about that first. Nick has had this stream on Twitch that he's been doing, and a while back he invited folks for these kind of data science roundtables on Wednesdays where he just invites people like me to come chat about whatever is current on our minds, et cetera. And we kind of joked about we both like cooking. And so we kind of joked like, what if there was something like Chopped, like the TV show, network TV show Chopped, but for data science, where instead of a basket of ingredients, you've got a data set. And instead of an hour to create a meal or whatever recipe, they're creating predictive models. We kind of laughed about it, but then we're like, well, why the hell not do it? So we decided to do a pilot season on Nick's stream and that was really well received and it was a ton of fun. So, yeah, that was kind of the impetus and where the idea came from. And then how does it kind of relate to kaggle? I think Nick and I are both really interested in data science community. So I do, obviously I work at kaggle, very involved in competitive data science. But for me, the bigger thing is the data science community. And I think that's what has really excited me about Slice and what's excited me about working, working with Nick on Slice, because he's just as excited as I am about that. So. But I'll let you, like, elaborate on that answer, Nick, because I know you've got. I'm sure you have your own thoughts.

No, that's pretty much all of it. I think the, like, the big thing in terms of coming together was also like. I don't know, I identified that there was like this missing sense of community in some ways. Like when it comes to like, you know, something that really. I don't know if it's annoying, but something that kind of annoyed me was all these, like, virtual conferences. And like, the thing that I loved about conferences was like, the networking and the meeting new people and being able to like, catch up with old friends and stuff. But when, like all these conferences moved virtual for the pandemic, a lot of that, like, stuff that I really liked about conferences got like, taken away. And so all of this stuff that I typically liked about, you know, tech and data science and all the different conferences that I went to kind of disappeared. And so I've been like, desperately seeking out, like, what was that sort of alternative? And there was really no good one. So I figured, well, let's just make one and bring together something that everyone can kind of rally around and cheer for people and get, really become fans of people in data science and things that you would typically find at conference socials or conference networking events, things that people can get behind. And for whatever reason, it's worked.

It's working so far.

So that begs the question, so for someone who has not seen Sliced, can you describe the experience a little bit, both for the viewer and also for the person who's participating directly?

Yeah, I could talk about it from at least the viewer perspective. I'll have Meg do the contestant perspective, but sure, but from the viewer. You know, I've been running my stream for a while now. I've been streaming since I was in grad school back in 2015. So it's been a while for streaming. And the community in chat, they just really want interaction. They go into these streams and like a really fun thing to do is being able to interact with the content creator or the streamer and having that almost immediate response back and having like, almost some say as to what's going on. It's really fun and clearly is like directly engaging. So when you put things in the context of like game shows or events and you have this like, you. You almost feel like you're a fan at like a sporting event and like if you have like front row seats or whatever, you're able to like almost talk to the people, like doing, doing the event, right? Like being able to see like LeBron James and having like front row seats and like, you know, maybe he like gives you a ball or like falls on you or something. Like it's exciting just to be that close. And, and this is one of those like things that I feel that people kind of respond to in chat is like, they're so close to like being able to interact with the content that's happening live and not only like interact, but they actually are able to participate live as well. So they. We have the data sets available in Kaggle. The contestants are, are using the Kaggle platform to submit and get scored by the competition as well as anyone participating. So they're quite literally doing all the same things as the contestants and they're able to like kind of joke around with each other and find each other and make friends through chat, make friends through like the Twitter hashtag and, and all sorts of fun stuff like that. So from the viewer perspective, I feel like there's a lot of engagement and it's like pretty natural because of just how the platform of Twitch exists as it is today.

Yeah, I would say I love kind of like the interactive, like different ways of interacting with Sliced as a viewer is pretty cool. But yeah, the perspective from the contestant is pretty different. Of course, it's probably really stressful. Huge props to our 16 contestants this season for putting themselves out there. And live coding in front of that's scary. A worldwide audience is what I like to call it. So, yeah, the kind of contestant experience, you know, Nick and I put a lot of work into recruiting contestants and so we've got 16 folks. So kind of like a little bit of maybe, I don't know, the behind the scenes, I suppose is we do everything through like Discord. So they're just doing like screen sharing and Discord and we have like a little bit of like kind of like a green room experience, like 30 minutes where we do like kind of like setup. And I get kind of like the vibe check from the contestants. And I know it's like some nerves and they're kind of like swapping tips and some ways that they're gonna plan to approach the challenge for the evening. So that's kind of cool behind the scenes to see. And then they kind of go dark. So it's like two hours live coding, which they can be fairly isolated, so they can't hear anything, they can't see anything. We don't let them watch the stream, et cetera. We do let them stream to their own channels if they have Twitch or YouTube. So folks like Jesse Mastopack, David Robinson, for instance, did their own streams on Twitch and YouTube respectively, emote only mode on Twitch. So they can be fairly in their own kind of lane for those two hours of live coding, all while knowing that their screen is visible to chat. So there is sometimes, I think a cool dynamic is the one way kind of communication that we see from the contestants. So they'll sometimes write comments in their markdown or a big thing, a big meta, I guess, for Slice has been memes. So one component of their points is chat vote. So basically I think it's like 5 or 10 points has varied throughout the season that they can earn through winning a chat vote. So we poll our audience basically at the end of the episode and say, who's your favorite? Basically. So our contestants are doing things like throwing up really funny memes that they know the viewers can see, even though they can't necessarily two way interact. So it's definitely a little bit of a performance that they're putting on in some sense. Then they live code, which is probably really stressful and nerve wracking, I imagine, as far as the experience goes. Meanwhile, I'm relaying messages and time checks to them in Discord just to make sure that they don't go totally off the rails and get in their own head. And then we invite them back to watch the Stream at the end of two hours. So that's always fun to kind of like they'll watch us do the judging portion. Nick mentioned that there's the points that we allocate for things like data visualization. Nick, does the leaderboard reveal. We sum up all the modeling points so they can watch that. And so it's kind of funny to see them then hop into chat and kind of start to interact with. Interact with each other, interact with, chat, interact with us. So that's pretty cool to see. And then of course, Nick has alluded to the community on things like Twitter, et cetera. So our contestants, even outside of the show on Tuesday nights, are also a big part of the community on things like Twitter through their own YouTube and Twitch channels, blogs, et cetera. So there's lots of participatory aspects, not just for our viewers, but for our contestants we've seen through the season.

So one thing you said very early on as you started going through it was you talked about spending all the time in your recruitment process, which makes me wonder, can you profile a little bit about what you're looking for? So we have all these data folks that are listening to you as this goes out there, and some of them want to be on your show. And so can you give them some tips about what your profiling is?

Yeah. Nick, do you want to take that one?

Sure.

We are looking for folks who are understand what predictive modeling is. So being able to take a data set and being able to put that through some sort of statistical or machine learning model package and come out with predictions, whether they're regression problems or classification problems. So be able to go from beginning to end with a data set into predictions, and then also be able to have some sort of data visualization skills. So whether that's really basic, like if you're in base R or using matplotlib or whatever it might be, or if you are, you know, really fancy doing things in Plotly or doing things in Bokeh or whatever it might be, crafting, like amazing interactive stuff. Whatever your data visualization skill set is, just have some. And with those two components that typically at least like in my personal opinion, like, that usually qualifies most like folks as like a, you know, data analyst or above kind of skill set. So that's sort of what we're looking for. The. Of. Of course, the. The big component of all of this is, you know, you only have two hours to do all of it. So whether you're, you know, a 20 year vet in tech doing software engineering and, and deep learning or if you, you know, are a student, the time is really, you know, the, the limiting factor for everyone. So you can't really get so deep into the, you know, neural net architecture. You can't, you know, really develop like a full end to end shiny app. But that does serve as sort of like, what can you do then? So on top of all that, we are always looking for people who are interested in participating for, for the next shows, and we have that on our site. So it's not too dissimilar from like a problem set you might get from like a job interview.

Yeah.

But it really is just a, you know, double check to make sure that, you know, you have the skills that's, that that would qualify you into, into sliced. And also, you know, you don't want to bring in people who are just gonna like, do nothing or do like, you know, maybe they, they think it's one thing and it's not and they have a tough time getting through an episode. We don't want to like, put people in a situation where they might be embarrassed or something. So we just really want to make sure that, you know, if you could get through some of the more basic problems or basic issues we've seen that most likely will, will, you know, we most likely will have confidence that you'll be able to get through the season.

Yeah. And I would say if people are interested, they can always follow along in one of our upcoming episodes after this podcast comes out. But they can also take any of the previous week's data sets on Kaggle and download the data set, make submissions, and really walk in the literal steps that our contestants did during the show and try to do that in two hours. And that'll give them a very real feel whether Slice is something that they might be interested in.

Okay, so I watched some even last night, and I have to say we were talking about choosing some of the contestants and that sort of thing. And from my perspective, in data science land, there were some like, LeBrons there as part of the competition. So maybe you could just describe a little bit about some of those people on the roster. And also, I mean, these might be names some people have heard of, but maybe you could also comment about things you've been surprised or felt like you learned about these people in the competition that maybe you don't get from just their public Persona.

Yeah, I think so. Some of the names you're probably alluding to is folks like Julia Silge, David Robinson. I would put Jesse Mostopak up there as a rising star, certainly on Twitch. I think we were able to recruit Julia Silge and David Robinson actually through Twitter. I think it got around that we were recruiting for Sliced on Twitter. There was some hype from the pilot season and somebody kind of like, egged on somebody else. I forget who started the egging on. You know, either Nick or I kind of jumped in, like, yeah, we'd love to have you. And then it kind of just like rolled, you know, kind of snowballed from there. But they were both up for it. They already, I think, you know, had started with some exchange of, like, some meme banter. So I was like, got some good vibes from this. This will be good. And I've worked with Julia Silgi in the past. Her and I overlapped in some time while we were both at Stack Overflow. David Robinson has also spent time at Stack Overflow, had worked with Julia Silgi. They've authored tidy text together, so certainly some networking. I also worked with Jessie Mostafak. She was at Kaggle for some time. So, yeah, definitely just some reaching into our networks to recruit some folks. Oh, yeah. The other part of the question was, what have we learned about some of these folks?

Maybe surprising things that you didn't expect.

Yeah, I learned that Drob will stop at nothing to learn something.

Yeah.

You know, like, he didn't know how to meme. And then he spent like a week, like, studying memes.

Literally studying memes.

So can he meme now?

Oh, yeah. Oh, yes, I think.

I'm pretty sure. I saw some. Some great examples last night.

He's a powerful memer. Yeah, yeah, yeah, yeah, yeah. And maybe he wasn't before. He definitely is now.

He went from novice memer to master memer just like that.

Some might call him like a 10x memer.

Yeah, he's a 10x memer. Yeah.

Oh, for sure.

We didn't. We didn't know before. Yeah.

Full stack memer.

A full stack memer.

Yes. I don't know if it's so much that this is something that I learned, but was maybe rather reinforced for me, but just how much some of these folks, like Julie Silge and D. Rob David Robinson are willing to just give back to the data science community and Jesse Mossbach too. Just give back to each other. Speaking of memeing, Jessie, I think, offered her coaching and meme skills to David, and I think they had some back and forth on, like, different meme approaches, and they have all kind of piggybacked off of Slice to create content and share content, learning and educational kind of materials with their communities. So it wasn't, that's not necessarily surprising for those folks, but has definitely reinforced, I think what makes them really kind of like great members of the data science community. And I'm glad that Slice has been a platform for them to do that.

I do think one thing that's pretty, I guess just universally interesting is through Slice, being able to see some of these rock stars of data science communication, being able to compete in something that's really foreign to everyone, I feel like it's really leveling or normalizing or humanizing even a situation because a lot of the times folks, maybe people listening to, to the pod right now, like we'll know these names only through content or know these names only through tutorials or books or, or whatever it might be. And you know, they, they think of them as like these, you know, masters of data science or masters of engineering. And it's really hard to think like, well, I'll never be able to do what, you know, Drob or what Julia or what Jesse or what whomever is on the like whoever is making content out in there. I'll never be able to get to that point. Like I'm all the way down here or whatever and through the show at least like being able to see like them not necessarily coming first place all the time I think has been like really eye opening because that's at least encouraging to some of the folks. I mean not just the contestants which like they've definitely thought that like I'm going up against Drob, I'm going to win.

You make a great point there. And you know, that's, if you think about it, kind of pre pandemic conferences when we were going and meeting and so much of the conference was not, you know, just the talk or anything, but all the things that happen in the hallway and dinners and everything kind of that same point is that that gives you that chance to realize that these, these people you've aspired to are human and they have to go through a thought process too. And sometimes, you know, as you're pointing out, you get to see them in action doing it and it kind of humanizes and also teaches you along the way. So I mean, that's a fantastic benefit. I do have a follow up question I'm curious about recognizing that these people clearly have the resources to do the job that they do for the purpose of the contest. Do they bring their own gpu, so to speak, or is there any sort of Leveling of infrastructure and equipment. How do you approach that?

Yeah, so one of the things that we kind of put together in advance of the show is a little bit of contestant guidelines. And with Slice being a pretty new concept, we didn't want to get super rigid with every single minutiae and kind of detail, but rather we wanted to provide some kind of higher level guidance. And one of the pieces of guidance was just don't bring your supercomputer, don't fire up TPUs for these kind of putsy little things. And just kind of like asking. Yeah, just asking.

You can't bring your own DGX into the contest just for yourself.

Yeah, so just kind of like asking, kind of like we're sharing. Here's the spirit of Sliced. This is the spirit of the competition. We just ask that you kind of keep that in mind when you think about what is your workstation, what's your setup, what are you going to use as far as resources. And if we ever get to a point where Sliced is on network television and we do have literal LeBron James becomes machine learning engineer and competes on Sliced, then maybe we will have to get a little stricter. Maybe we can provide resources to literally level that playing field. But for now we really wanted to invest in really solidifying what is the spirit of the competition, what is the spirit of Slice to make sure we convey that above all else. So far it hasn't been a problem. We've got a lot of people who are using resources already like Colab, which does, you know, resources like that do help to level the playing field and kind of like equalize access to data science resources, not just to our contestants, but for folks who are following along. Also should mention, mention folks could be using, you know, Kaggle notebooks as well, which provides free resources for compute. But yeah, that's, that's been our thought process. Hasn't been a problem so far that I'm aware of.

I mean the thing about the resources in general, even if you have supercomputing at your fingertips, because one of our contestants, Landon Buechner, he just graduated and so it's not. He just graduated as an undergrad. So it's not insane to think that if a student is on a campus that has access to a supercomputer and knows a little Linux and has, you know.

Yeah.

Access to whatever supercompute cluster they have, that's fine and like fine and dandy. But you only have two hours. So at the end of the day, like if you get to a Point where your, you know, training is going to cost you like hours and you can like limit that to minutes through supercompute then. Okay, fair, that, that's kind of, that might be a little overpowered. But our data sets are also curated in a way that the training is never going to usually be more than minutes. So like we, me and Meg do a lot on the back end to make sure that the data sets are a particular flavor. We're looking at tens of thousands of rows, we're not looking at millions of rows, we're looking at tens of columns, we're not looking at hundreds or thousands of columns. So even the nature of the data that folks are receiving doesn't necessarily lend itself to things like super compute, where like, in essence, if you did try to do that, you'd actually be losing time. So a lot of it is, you know, me and Meg making sure the QA on the back end is pretty good. Shout out to the engineers doing QA practice. So lots of qa. But also that plus the time definitely limits the resources and how powerful they could really be.

We'll see when we do the sliced computer vision spin off though. What?

Oh yeah, that could be. That could be good. Self driving slice.

Oh yeah.

Speaking of those tasks themselves, I know the one last night Chris is going to love if he doesn't already know about it because he's an animal lover. But could you describe, maybe just highlight a couple of the tasks to give a sense of what are these tasks that the contestants are working on?

Yeah. So last night's data set was our first multiclass classification challenge that we gave our contestants this season. So we gave them a data set of animals that were in animal shelters and they had to predict the outcome. Meaning was the animal adopted, was it transferred or was it something else? There's three classes that they had to predict and it was a data set of I think about like 70,000 or so. You know, it isn't tens of thousands of observations. And they had information about what type of animal. So most of them were cats or dogs. They had information about the coat color, whether the animal was spayed or neutered, how old the animal was, et cetera. So yeah, that was the data set from last night. Maybe I'll talk about one other, one of my favorites and then Nick, maybe you want to share one of your favorites or something. But one that was a big fan favorite as well as a contestant favorite was a data set about Airbnbs in New York City. And they're predicting this is a regression task. So they were predicting the Airbnb price. I believe this was a fan favorite for a few reasons. So really the diversity of the data set and the types of the data types in the data set are really great fodder for creativity, which really plays out in things like data visualization, but also feature engineering. So this data set had text fields, it had geospatial fields. So there's just a lot of really kind of like rich data types in the data set that our contestants had a lot of fun with. And I believe this is also the challenge where we used RMSLE as our evaluation metric for the first time. So our contestants and folks following along in the audience as well had to write custom evaluation metrics for this challenge, which apparently a lot of folks found really fun and interesting and led to some great content outside of Sliced as well. Like implementing RMSLE in Tidymodels was a blog post that Julia Silge published, I believe. And so that was a great data set just for its richness and the creativity that it enabled.

Any favorites from your end, Nick? In terms of tasks.

I see it more like a big picture thing. I feel like all the data sets are, you know, we've done, we're going to be doing 12 episodes. We did four episodes before. And like, for me it's like the consistency that we're hitting on data sets is like the global thing for me. And that every show has been pretty close to the same like competitive spirit, competitive advantage for everyone. And so there's not any particular data set that I have like my personal investment in. I'm, you know, my previous line of work was baseball. And so last week we had a predict home runs data set. So that was like near and dear to my heart.

That was cool.

But for the most part they're all, they're all really fun data sets. And you know, the QA and all the drama at the end in terms of judging, that's like the, that's where I get my energy from. So no, no real particular favorite it. I would say.

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So we've been talking about the competition and these contestants and, you know, the way they're doing it. But we haven't talked in depth about how you're doing your scoring and how the winners, I know that they have two hours to do it, but you have these amazing data scientists that are competing against each other. I imagine that some of them are everyone solving or multiple people are solving. So how do you get to that winner in this game? How do you come out on top against these other superstars?

You know, so many people have different approaches to modeling and just like any game, just like any sport, there's like a meta or like whatever your most most effective tactic available. I believe that's what meta stands for. So whatever the best strategy is usually is like what many of the people will do. And right now like we've seen like a progression from, from gradient boosted models out of the box to grid searching gradient boosted models. And then now we're in this Bayesian optimization grid search meta where the best models tend to be models that have Bayesian parameters on top of the grid search. Your typical grid search parameters. Right now like in terms of the modeling side, you see a lot of like heavy duty grid searching which to be completely honest, I didn't think we'd ever see in slice. When we did the pilot season, I thought people were just going to be like out of the box models and like try a bunch of them and see what works. But now people are diving really hardcore into like model tuning in the show, which is pretty impressive to me. And then usually it's like splitting hairs at the end, you know, like we've seen everything from like arbitrary weights and ensembling model predictions that just like gains a slight weight advantage in your log loss above someone else. And so people will try some like risky, maybe not exactly best coding practice but like, maybe good for the contest, ways to like reduce loss. And so on the modeling side there's that, but then like, you know, there's this whole data visualization judging side of things and that's where things get like really tough for me and Meg.

Can it get subjective in the sense of like you can have different strategies from different contestants and at the end they have different visualizations. So you're how. I mean that has to be tough. And when you're, you know, doing it live on the spot, how do you manage that?

It's very stressful.

I imagine it's super stressful because like we're watching them like we're watching them like, you know, paint it in front of us, right? And like, yeah, it's very different than like just walking into a museum and seeing it there, like opening a textbook and seeing them there, like, the layering and the iteration, it really gives you this other appreciation of this data, viz. And so, like, at the end, you do get pretty, pretty differing or even polarizing opinions as to who has better visualization and others. And like me and Meg, although maybe like half the time we're kind of in sync with points. There's a lot of the time where me and. Me and Meg will, like, differ. And I'll let Meg, like, kind of tell you all about.

How do you guys resolve that? I mean, that's. It sounds.

We don't.

We don't.

We just move on. I think over time we have become more in sync. I want to say, I don't know, maybe that's just wishful thinking or something, but, yeah, I mean, it's 100% subjective. And we have written out in the contestant guidelines that I mentioned earlier, we have given them some sense of the things that we look for. But really, I think there's three approaches overall that I've seen our contestants take to the data visualization portion. The easiest is probably just volume. So some contestants just go for. I'm just going to go for basic, all over the board kind of EDA kind of stuff, like just looking at different cuts of the data in visualizations and kind of otherwise keep things very basic. The second approach, I would say, is people who really go for the insights, which I think those are the folks that are looking for the data visualization points. So I would say people who stand out in this regard are folks like David Robinson. Also, one of our contestants, Josh Polecamp Hart, who competed in yesterday's episode, really do a lot of work to iterate and draw out true insights from the data, which is something that we look for and really appreciate. And, you know, they spend time kind of like customizing their plots to really make them really readable and following best practices as far as data visualization. And then there's the third, I think, category, which is folks who are driven to look for golden features. And we haven't talked a ton about golden features yet, but golden features are another way our contestants earn points, which is basically a couple of things that Nick and I have buried, so to speak, in the data set.

Easter eggs.

Yeah, they're like Easter eggs, kind of. What's a good example? Golden feature. Speaking of the Airbnb data set, one of the golden features was to take some of the metadata about the Airbnb listings and extract the number of bedrooms and bathrooms. Um, so it was to do a little bit of, like, text processing. Other times, it's to create some kinds of data visualization. So once one data set, we had them create like, a bump plot or that's like, something we wanted them to look for. Bump plot of like, a rank data set. And so if they. They happen through the course of their two hours of live coding to create one of these plots or these engineer their features in a certain way or whatever, that will earn extra points. So a lot of folks are really just kind of going for this, like, scattershot approach of, like, I'm just gonna find some golden features and hope that Nick and want to see me facet or want to see me use polar coordinates or want to see me do this or that with a data set. So it's kind of like a little bit like, you can tell that they're getting creative, and sometimes you see them sort of like, bend some rules of data visualization, where it's like, that's not maybe the most intuitive way to display that data, but you're just kind of trying to fit a square data set into a round, golden feature hole or something like that just to earn some points. So I would say that kind of like, summarizes the strategies that I think I've seen from our contestants as far as trying to earn points from data visualization. But I like it best when we see folks really looking for insights, because I think that's the purpose of data visualization. The same way that modeling the modeling portion, the purpose is to get the lowest log loss or whatever the metric is.

I have a little bit of a tongue in cheek follow up. And that is for the contestants. Which one of you are they most worried about, do you think?

Oh, it's Meg. We know it's Meg.

Yeah, I think I somehow earned some reputation early on that was, I think, reinforced through chat. I want to say I gave one of our contestants just one point in data visualization. So we have at that point, maybe we had, like, 15 points to allocate across the four contestants. Now we allocate 20 points across contestants, and I gave one of our contestants one point. And I think I said something like, you did data visualization. You checked a box, you created one plot. So it was kind of like a conciliatory. Just one point for doing it, but otherwise it was crap.

What's the guy's name? Simon Cowell. A little bit like that, Maybe just a tiny bit.

Meg is the Simon. Meg is the Gordon Ramsay Database.

There was another time that was very painful for me to give, I think with Julia Silgi, I had to give her two points. And I said, I'm going to have nightmares about this to give Julia Silgi, who is like, I revere her. She's incredible. She creates such beautiful data visualizations. When I worked with her, she's incredible. Her blogs are such beautiful. And then I ended up, I'm evaluating on your performance tonight and sliced two points. I feel terrible. But yeah. So I'm willing to be harsh. A harsh grader, I think, when it comes to data visualization.

So you mentioned that there's some unique aspects of learning that have come out of sliced. And I actually like how you tied. The inspiration of this came out of Chopped. Because even in the past, I think here with Chris on the show, I've thought I've sort of more compared data science or AI development in the real world more to cooking than to people think. You're sort of in your room with the chalkboard and you're writing really cool equations on the chalkboard or whatever. But I've always found it much more like cooking in the sense that, hey, here are your ingredients and a set of tools that are some good, some maybe not good. Figure out how to bake something that's not terrible. And the thing about industry is, like, it's always in a given period of time, right? You have a milestone or you're likely not going to have six or 12 months to refine the first version of your predictive model or something like that.

Because you have a boss.

Yeah, you have a boss. So I don't know how has the community engaged with that element of the learning? The ones that are offline, not featured as contestants? Have you heard anything from them as they're also participating in this challenge in that timed way?

That's a great question. I mean, I know anecdotally from Twitter, Nick and I religiously follow the sliced hashtag. It's probably embarrassing if I were to admit how much we follow it. But, yeah, I certainly see people are learning from the show and kind of going through the motions themselves. I think, though, we are seeing the same thing from our contestants too, and I think that is actually significant and it's bigger than I could have guessed or imagined. Just last night. Ethan Douglas, one of our contestants at the start of the season, we're in the playoffs now at the start of the season. He kind of just described himself as a data analyst and didn't have a tremendous amount of experience as a professional data scientist writing predictive models and he shared with me in kind of like the pre show green room, so to speak, that he has really matured as a data scientist more in the last couple months through Sliced than he has in the last few years as a self taught coder. And he considers himself now a data scientist. So he has really evolved and learned a ton from the show. I think our contestants are doing a lot of, some of them are doing a lot of practice in between episodes where they're making appearances and it's obviously a very strong potential embarrassment in front of a worldwide audience watching you live. Code is a very strong forcing function to learn. And so I think for folks out there who are hoping to kind of glean the same thing from Sliced, I think the message is to just put yourself out there and set some time boxed goals for yourself and go through this apparently is a great set of motions to go through to learn how to do data science end to end or it's a motivator.

Yeah. That's awesome. So you have a championship coming up, is that right? When is the, when is the Slice championship?

That is August 17th.

And how do people find, I mean I don't know how they could not find Sliced on Twitter, at least in my, in my feed. But how do people find Sliced and make sure that they, they tune into the championship?

You can tune in to Slice on Tuesdays. We have the semifinals next week, that's August 10th and then we have the championship on August 17th and that is Twitch TV. Nickwan datasci awesome.

And we'll put that link in our show notes as well. So make sure and click and watch and go to the previous competitions too and the data sets as well.

For sure.

Like has been mentioned. And can we look forward to future seasons of, of Sliced? Will this continue? Can you give us a little insight there?

Yeah, we have some stuff in the works right now and hopefully we will be able to share that really soon with everyone. So we're looking forward to not just Slice season two, but different forms of Sliced.

Even sliced in space. He's talking about sliced in space.

I assume that.

Just wanted to make sure it was clear.

Do you go Slice in space?

Maybe you could help, you know, hook us up with some Lockheed Martin help, you know, to get us there.

There you go. Maybe so. Yeah.

But yeah, we got some Slice news probably in the very near future and Slice season two, we'll, we'll take a breather and then we'll, we'll regroup and we'll figure out when we're gonna launch that sometime next year.

Yeah. I think, needless to say, like, Nick and I have been really, like blown away by the reception to Sliced and having a lot of fun. So, I mean, I want to keep doing Sliced season two.

Awesome. Well, I hope you both do and I know I will tune in and I'm excited to tune in for the championship. It's really exciting. So thank you both. Appreciate you joining us after a big day yesterday with with quarterfinals and yeah, see you. See you on Twitch.

Thanks, y'.

All. Thank you.

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Sam.