Jean Carlo: Data science, Machine learning, AI, Mentorship

In this episode of Behind the Journey, Prateek, Comms & Product Marketing Lead, is joined by Jean Carlo Machado, Data Science Manager at GetYourGuide, to discuss the evolution of machine learning and data science.

Jean shares his expert perspective on AI, multimodality, MLOps, mentorship, and much more. From dealing with the intricacies of computer games as a kid back in Brazil to tackling some of the most pressing challenges in tech. Jean also gets into the role of Data Products in business as well as the importance of community work. 

• (00:00) - Jean Carlo: Data science, Machine learning, AI, Mentorship
• (02:11) - Most exciting thought in AI right now
• (02:38) - Exploring Multimodality in AI
• (03:21) - Reflecting on AI Developments in GetYourGuide
• (04:42) - Challenges and Open Questions in AI
• (05:07) - The Journey to Production in AI
• (06:05) - Personal Use of AI Tools
• (08:07) - Tips for Non-Engineers Exploring AI
• (09:15) - Discussing AI Agents and Fine Tuning
• (13:51) - Reflecting on Early Experiences with Computers
• (15:45) - The Most Beautiful Programming Language
• (17:01) - Transition from Research to Development
• (18:54) - Mentorship and Its Impact
• (20:22) - The Role of Mentorship in Career Growth
• (20:53) - Lessons from a Tech Startup
• (21:55) - The Journey to GetYourGuide
• (22:40) - The Importance of Community Involvement for Developers
• (24:23) - Machine Learning 101
• (27:53) - The Role of MLOps
• (31:36) - The Impact of Machine Learning on Business
• (32:18) - The Role of Data Products in Business
• (33:27) - Knowledge Sharing in Tech
• (37:28) - Creating Memorable Experiences with GetYourGuide

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Creators & Guests

Host

Prateek K. Keshari

Comms & Product Marketing at GetYourGuide.

Producer

GetYourGuide

Unlock the world’s most unforgettable travel experiences with GetYourGuide.

Guest

Jean Carlo Machado

Data Science Manager at GetYourGuide

What is Behind The Journey?

Listen in as we explore and uncover what it's like to build the experience economy by diving deeper into the journeys of people making it happen, and getting a peek into their careers in engineering, product, design, marketing, and more. A podcast by GetYourGuide Careers.

Jean-Machado
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Jean: [00:00:00] How to transition from this kind of thing that this model that we have and it works here into these experiments but and move that to a real world scenario where there are data that you did not expect there is a scale that you didn't expect there is all sorts of real world kind of changes and this remains a challenge like how to get all of this technology and ship it as a product that is reliable and you can really trust on.

Prateek: How is AI changing the way we solve and approach problems? What's multimodality and why is it so exciting? What is the impact of machine learning and what's the journey like in data science at one of the most important travel tech companies in the world? Hi, I'm your host Prateek, and this is Behind The Journey by GetYourGuide, a show where we explore and uncover what it's really like to build the experience economy by diving deeper into the journeys of people making it happen.

Today I'm excited to have Jean Machado here. Jean is a data science manager at GetYourGuide and he shares a [00:01:00] fascinating perspective on ai, machine learning, mentorship, the importance of community, and more. He draws from his journey dealing with the intricacies of computer games back in the day, to now tackling some of the most important problems in tech.

I learned a lot from this conversation, and I hope you do too.

Prateek: This podcast is by GetYourGuide, a company on a mission to connect millions of travelers with some of the most unforgettable experiences around the world. Imagine local experts giving guided tours, skip the line tickets to your favorite attractions or exclusive bucket list experiences. GetYourGuide is headquartered in Berlin, and since its launch in 2009, travelers have booked more than 80 million activities through the platform, and all of it is made possible by a global team.

So if you're looking for your next role and the thought of making an impact on how people experience the world, sounds exciting to you. Head to Getyourguide.careers. That's Getyourguide.careers. [00:02:00]

hi, Jean. Welcome. Welcome to Behind the Journey. How are you doing?

Jean: Hello. Thanks for having me and doing great it's Friday. I'm excited for the weekend.

Prateek: That's good to hear. Coming to excitement.

Most exciting thought in AI right now
---

Prateek: Actually, I have a, I have a long list of questions for you, but the first one I wanted to ask is what is the most exciting thought that's living in your head at the moment?

Jean: Yeah, that's a tough question. But one thing, thinking on the spot it's easy to pull out is thinking around this area of multimodality in AI sounds very interesting for me. I'm pretty much follow the news and trying to stay engaged on it.

Prateek: Can you explain multimodality in general for everyone?

Exploring Multimodality in AI
---

Jean: Sure. It's this idea that with AI machine learning and so on, there is usually data is in forms of numbers or texts and so on, but how to bring the other senses to, the picture. So a picture indeed is one example of bringing images back to machine learning to solve all sorts of problems now with a little bit more of senses of the real world [00:03:00] beyond just bare data.

Prateek: Where do you see it going?

Jean: There is applications everywhere. I see that OpenAI, for instance, is doing huge progress on this and we are seeing all of this kind of and a lot of speed. Yes, exactly. This is a very research topic at the moment, but they are shipping products at the same time. So they are speeding up a lot of stuff in this area.

Reflecting on AI Developments in GetYourGuide
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Prateek: And you've been very close to all the AI action specifically at GetYourGuide can you reflect on the last one year? How has the landscape changed?

Jean: Yeah, there is a big kind of big positive surprise, I have to say.

Everybody's engaged about it, everybody's talking about it: society, government, everybody really. And speeding up indeed. Huge progress. Every week it's a new development and it's very hard actually to keep up. But at the same time, it's a much more inclusive space at the moment.

Like so much more folks that can deliver value . So pretty exciting, I have to say.

Prateek: At GetYourGuide itself, we had been using AI for a very long time. [00:04:00] How has that changed within the company?

Jean: I think we've been very much fast movers. There is a lot of momentum within the company.

Tons of folks engaged, tons of presentations, knowledge sharing, use cases, going to production or more exploratory topics as well. So yeah, we are following the kind of the big movement that's going on and learning a lot. Some of our practices improved but a lot is also much the same as before.

And given that this space is so exploratory, there's a lot that are open questions that need to be figured out. And we can use this learnings from the way we've been doing data products. Before and bring it back, like best practices and so on.

Challenges and Open Questions in AI
---

Prateek: What are some of those open questions?

Can you go deeper on that?

Jean: There is so much about trust and safety, like what you're shipping to production, can you really control and and how to observe what's going on, how to assert that it meets certain criteria of quality and so on. All of these [00:05:00] questions tie back to best practices, how to go to production, which is yeah a big topic dear to me as well.

Prateek: Yeah.

The Journey to Production in AI
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Prateek: Can you expand on just the challenges of, in general, going to production and GetYourGuide has a huge scale. And as an industry as well what are some of the challenges that you've been hearing from other people in different industries?

Jean: That's a good question. Machine learning has started as much of a academic pursuit and there was at some point before MLOps become a big thing and still a problem that, how to transition from this kind of thing that this model that we have and it works here into these experiments and move that to a real world scenario where there are data that you did not expect there is a scale that you didn't expect there is all sorts of real world kind of changes and this remains a challenge like how to get all of this technology and ship it as a product that is reliable and you can really trust on it's a big [00:06:00] topic. And I don't think that we will run away of problems to solve in any point.

Prateek: Yeah, that's, that's exciting.

Personal Use of AI Tools
---

Prateek: Have you, what has been your usage like of in general, like personally from productivity standpoint of AI tools?

Jean: AI tools? Yes. I mean, ChatGPT is so powerful, right? I think it was a big surprise on how much even like just navigating German bureaucracy becomes so much easier now with using these tools.

Going beyond like more to development tools, yeah, a big use of, big user of Copilot. Yeah. I'm also a big believer in building your own tools. So use the SDK here and there and solve specific problems Yeah, using this kind of technology. So yeah, I think I don't have any kind of super nitty gritty specific tool beyond the ones that I built for recommending here just the classical ones stick with some sort of complexion tool ChatGPT itself

it's very powerful.

Prateek: I think the developer community is also shipping things so fast. With [00:07:00] respect to AI tools, I remember there was a few months back there was a talk about this tool called Cursor. It's a, it's basically a fork of VS Code. And Cursor is amazing. One of the things that it does really well is like you can use your own OpenAI API key you can put in and you can use 3.5 for GPT 4. But I think one of the best things I like about it is that that it has indexed documentation. So you can chat with your code, but you can also reference documentation, which makes it much more reliable in terms of output. and I think there are going to be more tools like that in general.

Jean: I think the great products are still being built. But indeed there are some kind of obvious problems that need to be solved, but then one needs to really make it very well polished to really solve the problem. Because one topic for instance on like you get an error on your system, how to move from that very descriptive error message to solving that problem. A tool that is language equipped with [00:08:00] large language models can definitely have a very good chance of solving such type of problems, but the products is still not there, but it will come for sure.

Tips for Non-Engineers Exploring AI
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Prateek: Any tips for people who are exploring AI and maybe non engineers in general, how can they keep up? How can they, for example. look at the seismic shift that's happening and make use of it in there interest?

Jean: I like a lot of these area of agents also that's solving very narrow problems deeply. But at the same time, tying a little bit more to your question, I think. The ecosystem will evolve and new tools will be available. . I would stick a lot with OpenAI at the moment as they are clearly on the lead. And try to use ChatGPT for your problems as much as you can. Yeah, following some new sources and so on, to stay on top, there is all this assistance on like other tools that I think will be exciting. If you're using some cloud Google Docs and so on. There is. It's already some prototypes on Google. Yeah, these things will get much better [00:09:00] over time. And so whatever you are doing in the tools that you use. Are there tools that are already solving it with AI enabled to make it even easier? Or in the same class of tool or maybe your tool already has and you just need to learn it.

Prateek: Can you expand,

Discussing AI Agents and Fine Tuning
---

Prateek: you briefly mentioned about it. Can you expand on agents

in general?

Jean: Yeah, so the prompt format is open text, no context, or you can add a little bit of context in the text format. In the applications we are using, there is a lot of context. We are using an application for a specific purpose and it has connections to other tools.

Prateek: Can you give an example? ..

Jean: Google Docs, for instance, has connections to presentations and so on. Or to your drive with a lot of your data. Agents will play a big role in connecting all these dots, and solve very well a problem, not so much into kind of starts from first principles where you have to outline the whole textual problem, but they will use a lot of the context and solve problems for you. I'm pretty excited about that..

Prateek: Yeah, [00:10:00] that's very cool.

And on context specifically, there's also a lot of industry talk about fine tuning versus RAG retrieval augmentation. Can you walk us through what does that mean? And what's your view on it? When to use which?

Jean: Yes RAG specifically is not the part that I focused a lot.

So it's my personal opinion, but my view is that RAG is for retrieving documents and where you have a effect that you are trying to retrieve. And so you can basically use RAG to get you to the right place. While fine tuning as more generic, right?

It's like changing your model to, to become better at certain tasks. You could use fine tuning for improving the performance of a RAG system by adding more context and so on to make it better. So I think they fulfil different use cases and they can be even used in conjunction.

Prateek: Can you give an example?

Jean: Yes. Let's say we are building a chat bot which has frequent asked questions from like a support system.[00:11:00] One can basically use ChatGPT and use RAG over the documents to basically have a vanilla version of RAG on ChatGPT or you can fine tune it. Basically your model, let's say ChatGPT for having your tone of voice or something like this, and then using these two things in conjunction, you're going to retrieve documents and the answer to your questions will be even more tuned to basically...

Prateek: Similar to what OpenAI recently released, custom GPTs where you can upload your knowledge base and then have it act in a certain way.

Basically a much more tuned version of a chat, for example.

Jean: I think there though, there's no fine tuning behind the scenes. I'm not so sure because I'm not totally on top on this. But indeed, you can improve performance or factors that you also care about like tone of voice or structure if you need some sort of structure in your output.

Prateek: And

fine tuning?

Jean: What about it?

Prateek: An example?[00:12:00]

Jean: Ah fine tuning. There is many reasons one can use it. I think from the AI perspective, the most common is the tone of voice is I think the core one that open AI also advertises a lot. But you can remove the head of the model for any kind of tasks like a text processing task, like you can start doing classification with a large language model. So you would fine tune that large language model in a classification task and it would suddenly start not being a generic model anymore, but like a classification system. So there is many reasons to do it.

Prateek: So if I understand correctly fine tuning is more about teaching the model to do something truly novel, so something new, that it does not know. In general, right?

Jean: Yeah, exactly. One can teach it something novel or influence it to do something in a slightly more refined way, specific.

One indeed changes the behavior of the model in a more fundamental way.

Prateek: What's been your experience, and I know you fine tune[00:13:00] a lot, so how has your experience been just fine tuning models for your use cases?

Jean: It's early days and the field evolves a lot.

There is different kind of ways one can do it. One can, use OpenAI and then you have this API experience, but you lose all the control. Although it's probably a great way to start for many problems that you want to solve fast. If you want to go more deep fine tune open source model like Yammer, then yeah, that it's also getting better and better, easier and easier.

There is a couple of tools that are becoming very standard, like Hugging Face. Of course, you need a GPU. You could also do without it, but it's so much harder that I don't count it as an option. And then there is the standard process as well. It's very much like machine learning, more classical machine learning, but it involves a little bit more hardware and some steps of

Reflecting on Early Experiences with Computers
---

Jean: preparation.

Prateek: Got it.

So when we were talking a while back just before the podcast, you mentioned games and complex systems and how they sparked your interest [00:14:00] in computers. How have these early experiences reflected in your current approach to problem solving in general?

Jean: That's a very deep question. Let me try to unpack it.

I think the problem solving component on it, games they are very kind of logical things and you can see with simple rules, you can have very complex behavior. And back in the day also, when I played games, in my first experiences, I had a very bad computer and I had to hack it around to make things work.

Prateek: Take us back to that time where how was it, like where were you what stage of your life were you on?

Jean: I was maybe 13 years old. And got this old computer and...

Prateek: do you remember which one? Do you remember which one?

Jean: Um, I mean, back in the time, I guess there was Diablo 2, Age of Empires. These were the things that kept me hooked. Yeah. And basically. One had to hack a lot of stuff to get it to work in my Pintune 2. Yeah, it was a great learning experience. Even though I didn't know it conscientiously, like I had to [00:15:00] go into the OS and figure how to kill every other process that could be killed and make sure that things would still work.

Yep. I loved that time and there was a lot of energy for me into this kind of real time strategy games. Simple rules, complex systems, which kind of kept me hooked. Somehow I managed to then invest this energy that I found there more into kind of programming once I got those skills.

And yeah, that's give me also a lot of confidence when programming after doing so much on the side exploring my own ideas, which I think was very helpful and it's the same energy that comes back then, like looking at this systems working.

Where were you at that point in time?

Were you in Brazil?

In Brazil. Yeah

Prateek: And you mentioned programming in general. I'm going to ask

The Most Beautiful Programming Language
---

Prateek: you a controversial question. What's the most beautiful programming language?

Jean: Yeah. Depends on which criteria, I would say. I have done my share of exploring programming languages. I had a brilliant friend at some point that was really genius [00:16:00] level into programming languages and I got super inspired from him and I did my own research and explored really tons of the programming languages and my conclusion, after doing that for a long time, was simple is the most beautiful, like solving problems fast and reliably.

That's that's a superpower. To come back. A big fan of Python. I also help the community in ways that I can, organizing PyData Conference and I'm happy that data science and Python also found like a perfect match there. And that's from the overall programming language question, but if you would ask from purity perspective, like of mathematical purity, for sure, the answer would land more kind of Haskell or Elm or languages like this.

Prateek: As a non, as a non dev, I think I also find Python to be very inclusive and accessible as a language. And the community is so strong.

Jean: There is a lot of beauty on that also. It's an open community with open values and yeah, that it's a big plus.

Prateek: Yeah I agree with you totally. And [00:17:00] you transitioned from

Transition from Research to Development
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Prateek: building really complex things, also from research. You were doing research back in Brazil, right?

Jean: Not exactly. It was more a, in academic context, building hardware or like prototyping hardware for like accessibility use cases.

Prateek: Okay. Can you expand a bit more on that?

Jean: The biggest project that I was involved there was around like a Braille reader. Some pins, hardware pins, that goes up as you are reading something in the computer.

So the blind person can basically, yeah, read the computer, what's going on. Oh, nice. Yeah. The project was not really successful by the time I left. I stayed not so long. Yeah. Yeah, but I designed the kind of electronic prototype and that worked well and they were happy with the results.

But the toughest part to scale at that point was the hardware and I had no clue about that. It was being developed on the side by other folks.

Prateek: And then you transitioned into a dev job. Yes. How did that transition go?

Jean: I think the context is rather different, right? You are into this kind of more [00:18:00] research kind of exploration, which is very low level, more going to electronic side. I studied electronics for some time. And then moving to programming, as main task, and then it's about building things reliably fast. I think knowing the low level really gives you an extra hint on how to build things in the right way. Yeah, that certainly helped.

I really liked to go higher level over time. First, I was really passionate about, oh, the bytes here going through and there. But over time, it became all about how can you solve this problem. In the right way.

Prateek: And how long have you been doing this now?

Jean: Yeah, more than 10 years.

This was 2012...

Prateek: Yeah, and when did you move to Berlin?

Jean: 2018.

Prateek: Okay. Did you move for GetYourGuide, by the way?

Jean: Yes.

Prateek: Okay.

Jean: Yeah, six years now.

Prateek: That's amazing. Half a decade. Yes. More than half a

Mentorship and Its Impact
---

Prateek: decade. Yes. That's great. And we all learn from other people, mentors [00:19:00] and whatnot. Have you had those guiding figures in your life who you have learned from?

Jean: Yes, one very instrumental person in my career was Elton Minetto he's a big, relatively to the kind of space that we are like in the software kind of space, he's a very well known in Brazil. Particularly there in the, he was back in the day, in the PHP community and he was a superstar there.

Okay. And nowadays he transitioned more into Go and more into tech leadership conversations. Yeah, he was really instrumental, very early my career. He give that push on going to conferences everywhere and presenting and connecting with the leaders on, on that field.

And getting to know the kind of people that were moving the needle for that community and yeah, was really very good for me.

Prateek: In general, with events and conferences, what were your biggest learnings as a mentee?

Jean: He teached me a lot about learning through teaching.

So if you want to get good at something, it's a very effective way if you [00:20:00] have to teach it to somebody. Also that most of the people around you, they are struggling with the same stuff, but they usually don't talk about it so much. So if you give a presentation, you're going to find tons of folks that can relate with the problems you're facing and then you can discuss things and move forward.

This very open community mindset of open source also was, very much

The Role of Mentorship in Career Growth
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Jean: tied to this culture.

Prateek: And do you have current mentors? Do you actually mentor at the moment to anyone?

Jean: On my current role as a manager, I see is there is a relationship here that I'm very much committed to their growth.

How can I move the needle for them further? Yeah, when I was more in an individual contributor role then I had more kind of a formal mentorship conversations. Pretty busy with parenthood right now, so I will go back to it. But it's good to have a tiny break at the moment.

Prateek: Yeah, for sure. I totally

Lessons from a Tech Startup
---

Prateek: understand. And there are key lessons that you learn from people when they mentor you and both as a [00:21:00] mentee as well. There was also a part where you were leading the technology part of a startup under his leadership and mentorship.

What, if you go back to that time, what are some of the things that come to your mind that really paved the path for you for the rest of your career.

Jean: He teach me a lot about how cloud solutions pace, like how to build systems for the cloud with this scale that you can solve problems for the entire world yet be very simple. The problems ideally are complex and the solution is simple. Trying to really keep the simplicity. There is a lot of beauty there, and I learned a lot about it as well from him. Also about how to deliver value, iterative and, going deep into systems and understanding all the components that, that moves a cloud environment.

Yeah, tons of learning.

The Journey to GetYourGuide
---

Prateek: All right, you mentioned about moving to Berlin for GetYourGuide, and you also mentioned [00:22:00] about PHP. There's a connection there. Take us back to the time when you joined GetYourGuide as a PHP engineer.

Jean: Yes. So under his mentorship I could very much speed up my career and get very deep into the core communities in PHP and know everybody.

So it was relatively easy then to land a job in Europe. If people can find you everywhere online, like in the right communities, then as a developer is a great way to get a great job. I was pretty happy on the startup there. There was a lot of progress it was

very hard to fail because there was a big company behind it. But I wanted a little bit of adventure, quality of life of Europe, and the cool domain of travel. All these things play

The Importance of Community Involvement for Developers
---

Jean: the role to make it decision.

Prateek: You constantly stress on the topic of devs being a bit more out there. Going to conferences, doing meetups, writing blogs, things like that. Knowledge sharing is a big component, I understand, that you're stressing on. Both to gain new perspectives when you share and also to share your perspectives on [00:23:00] things. For people who, who're just starting out in these cases or are not that out there specifically, what are some of the things that help you be a little bit more out there, support communities, also do so much at your job and give back.

Jean: Yeah, great question. I think you have to figure out why you are doing it, if you are helping to improve the status quo. So many systems out there being built. I was very much into kind of Linux at the beginning of my career. I want to become a kernel developer. But yeah, in part because, some folks that brought that core kernel kind of parts, their code is the impact of that is like huge, if you make a contribution to one of these core projects, billions of people all day are using your code.

So I think with computer science if you are really being driven by this kind of impact that you can have in this field, then I would certainly recommend people to meet other people to start establishing a connection build some hobbies kind of stuff that [00:24:00] interest you naturally and just try to play with that idea a little bit, make sure that this work is somewhere visible, that other people also can reach it out, like GitHub, of course, it's one.

Building this portfolio of things that you are interested about. It's over time that, that basically becomes a lot of evidence that you are very much interested in stuff and people will reach out and want to do stuff together.

Machine Learning 101
---

Prateek:

Let's talk about machine learning and let's do a machine learning 101 for everyone. Can you explain what it is, why it matters, and why should teams care?

Jean: Sure. What it is, it's yet another way to solve problems that otherwise could not be solved. With programming, you can do a lot of kind of specific things, but like following recipes. But in machine learning, if you put some data together, you can start looking into predictions.

You can start looking into how things would look like if a certain scenario is true or false, and that's extremely powerful in all sorts of [00:25:00] scenarios, right? It has such a big potential to use data in health care or education, like building predictive systems that can tell you what is your tendency for certain diseases or how to prevent things even ahead of time. Education as well, how to tailor education to your specific knowledge gaps or your culture and make the most out of your specific needs. There's a lot of statistics there looking to historical data on how these things looked for other people

and you can really make a dent into this problems at scale, so everybody benefits basically. So, that's more kind of society part, but of course for business is extremely useful as well. We can solve all sorts of business problems at scale with these tools. It's also can be seen as Software 2.0 right? That Andrej Karpathy talks about in the famous blog post that the space of things you can solve with software in a Venn diagram is x but there is a much bigger space that you can solve with data and machine learning and AI too.

Prateek: For those who do not [00:26:00] know about him, can you?

Jean: I think he was head of AI at Tesla at one point. And he was involved in OpenAI, I'm not sure how. He has a very good tutorial about how to build ChatGPT and stuff like this.

Prateek: I think he published that in I think it's a one or two hours video, "How to build your Custom GPT" or "How to build an LLM" or something like that. I find that insane. Like he put all that knowledge out there for people to see in a YouTube video. It's also pretty popular. I see that it has millions of views.

Jean: I did that tutorial. Yeah, it was brilliant. I think it speaks a lot to the moment of this openness of knowledge and like that it makes things much more inclusive.

There's other perspectives. People think also, okay, there's these big companies that own all this hardware and they control everything. That's certainly something to watch out for. But actually I feel that there was so much more inclusivity going on after the introduction of this tool.

Prateek: How have the last 12 months changed for machine learning in general?

Jean: [00:27:00] I mean in different ways. As I mentioned in, in the beginning there's a overlap of forces, standard machine learning can bring a lot of good practices to the LLM space because there we already figured out so much but the other way around as well, right?

Like the power of the language can unlock so many new opportunities and possibilities. That these models are so good with language, you can solve some problems much faster, although reliably still open question. But we are okay on this and making progress. So there is a lot on this aspect of just unlocking possibilities that before would be too costly to build, or you would take so much more time to build it, or much more people, and now it's reduces a lot of the complexity and allows people to focus more for solving the problem rather than all these kind of technical sub problems that arises when you have to build

The Role of MLOps
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Jean: this stuff from scratch.

Prateek: You've been pretty active in the MLOps community as well. How did you get involved?

Jean: Yeah, that ties very [00:28:00] much into my transition from a PHP engineer to more into the machine learning field.

Three, four years ago, people realize that actually there is a new field here. There was a lot of talks before about Google has been doing this for much longer than four years and the other tech giant. But I think that there was like the realization that this was important for business because a lot of people wanted to put machine learning into production and didn't know how.

And there was a long time where there was a model of throw over the fence problem. That you assume that there is a data scientist with the particular skill set. They solve the problem and then they sent to the engineering team and that they solve the problem. But actually, there is a better way to do it, which is you keep the ownership within a data science team of going to production and you try to understand what are the needs there and build the tools and the systems around that fulfill those needs.

And there's quite some particular needs to go to production with machine learning. Yeah, so GetYourGuide was in that point as well. We were struggling to put ML to production. And MLOps [00:29:00] arises this topic. There was the Metro's kind of leading this very famous community podcast back then.

It was mostly a podcast, the guy is super funny. And I just, I messaged him at some point, "Hey, do you want to come to talk to us at GetYourGuide about the stuff that you're doing? I think there is overlap." We met, then he come in our online conference and we had a great time. And eventually we decided to create a MLOps position at GetYourGuide.

I transitioned to there and I've been connected to the MLOps community so far. We created a Berlin chapter of the meetup. Yeah, tons of learnings. We ran a lot of meetups. The first one in GetYourGuide. The second one was in Wolt. The third one was in Google. The fourth is going to happen soon. So looking forward to that.

Prateek: Can you expand on the role of an MLOps engineer? What does a day

to day look like? What are the challenges?

Jean: I think it has to go back to what is MLOps, right? It's everything around trying to go to production with machine learning.

So it's not about [00:30:00] modeling, it's about what are the tools and the ecosystems that one need around it, like which systems needs to be in place to do it reliably. So the MLOps engineer will basically support data products teams. So we're building machine learning production to serve the customer in data products.

So the data scientist has a modeling problem, how to fulfill that use case with the systems? Which systems need to be in place to get the right data? At the same time to make sure that the data remains. You need to observe it. So to make sure that the data remains constant, you need to basically figure out how you do machine learning.

There's a lot of model features which you get the data, you transform it in a right way. And that has to be retrieved in the right way, also in production. So one needs to figure out how to do that for production use cases where you have maybe thousands of requests per second, or you have a huge scale or on the data load, like terabytes of data to process. So how to retrieve data correctly, how to make [00:31:00] sure that it's observable, how to make sure that your system that uses machine learning is optimized to be fast enough, because that's a compute bound problem usually, so you have to do some compute and return the results. And there's tons in the particular models that needs to be optimized.

Yeah, looking into how to run it reliably in production, keeping costs at bay, keeping scale ready which tools needs to be in place.

Prateek: Can you take a real life example for GetYourGuide and explain what does a day to day look like?

Jean: One of my favorite problems is the real time ranker.

The Impact of Machine Learning on Business
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Jean: For each activity you've seen, GetYourGuide's website. There is a system that after these activities that can fit somehow that location where defined. There is a system to define which order those activities should appear. So what comes first? And what comes first is usually, has to be what you want.

And so there's a system that takes input[00:32:00] about your context, where you are, the kinds of things that you've been interested in at GetYourGuide so far, and basically figure out which activities to show you next. That's a very high scale, right? Like thousands of requests per second and in the high peak.

And there is one machine learning model basically deciding that question.

The Role of Data Products in Business
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Prateek: What fascinated you about data products? You mentioned briefly about it at GetYourGuide. And also what can you tell us a bit more about the team in general?

Jean: Yes, well what is data products?

We are basically building machine learning solutions for solving customer problems and yeah as short as that. So looking into basically the customer journey and all its experience and figure out how data can make that better and optimize it to make it more relevant or, get you the right content in place at the right time.

That's basically what this team is about. I think what fascinated me about this area in general is indeed the [00:33:00] natural kind of inherent complexity of the problem and the real impact you can have with it at scale. So you can really change the experience of so much people with, yeah, looking at the right problems.

So there's a lot of emphasis in problem definition, figuring out how to crack that in a sort of a mathematical way also. So I like the inherent complexity of solving problems like this and having them at scale. So I think these are kind of ways that I got attracted to it.

Knowledge Sharing in Tech
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Prateek: In complex systems, like you work on, what's a decision making framework like? How do you make decisions? How do you choose not just what to work on, but how do you decide what's worth it in general?

Jean: Yeah, we tie a lot back to GetYourGuide values and this emphasis in impact. Yeah, that is a big lever. Also, strategic considerations is important for the business, not only now, but in five years or longer.

And how we decide on if we are successful or not, as part of the rest of the tech organization, we are very much experiment oriented and data [00:34:00] oriented. So there's a lot of A/B testing going on. That's the way we operate. And in data products we are saying now, more often than not, we have superpowers of like knowledge sharing.

A lot of trust. And a lot of knowledge sharing. Yeah, that's really helps a lot and makes it very engaging to keep trying to solve problems there every more kind of impactful and so on.

Prateek: By knowledge sharing, you mean you have sessions weekly and things like that?

Jean: Yes, we have a great collaborative culture, a huge overlapping values and the same kind of passion for core kind of topics, and ceremonies, and we try to look into the way we work together continuously to make it as streamlined as possible, and a lot of knowledge sharing through, presentations and meetups and and so on.

Prateek: What are some of the key skills that and qualities you look for in, in your team members?

Jean: The GetYourGuide framework helps a lot here. Having this cultural values[00:35:00] it's important having a big overlap in them, having an impact oriented mindset.

So how can you make the biggest change for a customer? Ownership is also important. What you build, you are responsible for. And if it needs to change, you are also responsible for that.

Prateek: Can you expand on that a bit more?

Jean: Yes. This goes back also to the topic I mentioned with MLOps that things has been done in the longest time to go to production with machine learning through throwing over the fence the problem and suddenly becomes you build something the other person has to replicate that in another system to basically make it work. And we managed to change that to, you build what you want.

So as a data scientist, you have a lot of ownership on the model that you build. We put tools in place that you can also observe how that changes over time and a lot of automation. So for instance, there is this concept of drift. Let's say the world change that there is now, I don't know, one market becomes more popular than the other, their seasonality, all these things influence a data [00:36:00] product, right?

So one needs to be on top of them, performance can degrade over time. So ownership here means, we build something and we keep iterating on it and make sure that it keeps working the right way.

Prateek: How much of that is also about the landscape in your field changes a lot. What advice would you give to people to be on top of things in general?

Jean: I think being really driven by your core questions, looking for what is that and how that can overlap with what is going on around you. And that naturally creates energy to basically go out and and meet people or learn a new topic. Also having this mindset of sharing the knowledge, right?

So if you're constantly looking, how can I add value here? Which topics will be important in some time. Yeah, this continuous interest about the field that leads to a lot of drive on itself and a lot of energy to continue doing it. And things get easier to do over time.

So it's rewarding also to see we're making progress, right? It's not [00:37:00] only about yeah, collecting more information, you're solving problems better, you're solving more problems or at a different scale.

Prateek: What do you do when you hit a roadblock?

Jean: Continue talking I would say there is always so many other options and continue brainstorming. There is always alternatives. We sometimes feel we are stuck, but it's just give a step back and really the stuck situation is often the case, not really there.

Prateek: Yeah, cool. Last question.

Creating Memorable Experiences with GetYourGuide
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Prateek: What has been your more recent GetYourGuide activity.

What did you do?

Jean: Ah! I had last year a very special, no it was this year, a special vacation. My granny came from Brazil. First time in a commercial airplane, actually, so changing continents. Yeah, she stayed three months here and we went within this travel also to Italy as she wanted to see the Vatican and the Pope and so on. So yes I took some, yeah, quite some stuff there with GetYourGuide, and Vatican museum was quite memorable.

Prateek: [00:38:00] Take all of us back to that moment. How was it?

Jean: It's breathtaking to see so much history, yeah like quite memorable trip, a lot of beautiful moments.

That also resonates a lot, right? With the GetYourGuide way that so much about memories. And this was really one of the best highlights of quite some recent years.

Prateek: What did your granny think?

Jean: She loved it and she's coming again next year.

Now she wants to go to Paris.

Prateek: Paris is a beautiful city.

Jean: Yes, we're going to take her there.

Prateek: Very cool. Very cool. Thanks so much for coming in today. It was a pleasure talking to you about so many things.

Jean: Thank you for having me. It was a lot of fun.

Prateek: Thanks so much and have a good rest of the rest of the week for you.

Jean: Likewise. Great fun here. Thanks a lot for having me.

Prateek: Thank you, John.