You're having a conversation with one of your early adopters of your restaurant platform, and you're like, what else do you need in here? What else do you need in your restaurant discovery journey? They're like, you know what? To eat at these restaurants, I'm going to need a high paying AI job. Yeah, that's

exactly how it happened.

with say about four people and two production machine learning models. But most of the industry best practices that we hear about are from a small handful of companies operating models at hyperscale. The folks over at Neptune .ai care about the 99% and so they are changing the status quo by sharing insights, tool stacks, and real -life stories from practitioners doing ML and MLOps at reasonable scale. Neptune have even built a flexible tool for experiment tracking and model registry that will fit your workflow at no scale, reasonable scale, and beyond. To learn more, check them out at neptune .ai. That's neptune .ai. Well, is mentorship crucial? Do you think? I mean, I'm kind of leading the witness here with this question, because I have a feeling that I know what the answer is, but you can elaborate. So is mentorship crucial for career growth or career entry into data science? Or a better way of phrasing that would be, is that more so the case in data science or

software engineering than in other kinds of fields? That's a really good question. I think honestly, the answer is no, mentorship isn't crucial period. I know that's not like the full question you were asking, but some people can just have the personality where the way they motivate themselves, you know, they can look up blog posts and do personal projects without any kind of oversight, power through all the obstacles in their way. And that's great. Like if that's you, you have the right to recognize that and you do not need necessarily a mentor, you might not even need a bootcamp. So different solutions work for different people for sure. I think one of the things that makes data science so conducive to mentorship is how fast it moves. So like imagine, even if you did like a PhD in machine learning or a master's in data science, which is a really common thing nowadays, and you graduate from this program at the time you started the program, there are libraries that like did not exist when you graduate, like, or will not exist when you graduate. And quite often these libraries end up being really important. Like every, like all of a sudden, Streamlit, everybody's using Streamlit, like that, this becomes just the bar. And so you need to keep up with the stuff. You need to know what's relevant, what isn't, and to find a way to figure out as well in this giant space of so many different tools that are constantly evolving and disappearing, you're like, which ones should you actually focus on? And so I think having the advice of somebody who's actually in the industry to help you navigate that landscape is really important in a way that it's not in like, you know, if you think of a field like, I don't know, like nursing or something, you know, this is something that's the way that nursing works hasn't evolved quite as fast as software development or data science. So for those more technical fields, I think you really do benefit a lot or disproportionately from mentorship. Not to say that it's not valuable in all contexts, but the I would say the scope of it is much bigger. The scope of the value you can create through mentorship is much bigger in a space where things move quickly, where you can ping people who are actually living and breathing the stuff professionally.

Nice.