How to Learn ML in 2026 interview

I had the pleasure of being interviewed by Yacine of Deep Learning with Yacine. He posed a lot of very thoughtful questions and I had a great time. Please check it out!

The lightly edited transcript follows, auto-transcribed with faster-whisper and then reviewed by me for correctness, adding links for context, and editing out a lot of “I think” and “um” and “like” selectively.

(Yacine) A lot of people asked me how to get into machine learning in 2026. Instead of giving you my usual advice, I figured it’d be way more interesting to sit down with expert contributors who have like a diverse background and ask them what do you think about that. So today we’re chatting with the legendary Vicki Boykis. She’s an absolute gem if you know her from Twitter and Bluesky at giving well-grounded advice that are full of pragmatism. She has a pretty impressive career so far. She built a recommendation system at Tumblr. She worked on the ML platform at Duo Security.

She worked on LLMs at Mozilla.ai and today she’s a founding ML engineer at Malachyte, a New York startup doing real-time search and personalization for e-commerce. The cool thing is that she’s actually self-taught and actually started with a degree in economics. Her first job was in trade consulting which she described as just doing a lot of Excel spreadsheet stuff. Then she noticed her co-worker automating their analysis with R and SAS which I haven’t heard in a long time.

And she asked herself how do I do that too. That question, how do I do that, pretty much drove our entire career. After first asking herself that, she then did an analytic outcome cast, then she went into data science and then machine learning engineering. Every time she saw someone one step further down this machine learning kind of career pipeline, she went and learned what they were doing on their own and then internalized that skill. So I asked her this simple question which is if you were starting learning this stuff in 2026, how would you actually do it?

It was a very insightful and pragmatic conversation and I hope you enjoy it.

(Vicki) I graduated with a B. S. in Economics. So nothing to do with machine learning or programming. I started working in analytics and international trade consulting, which is a fancy word for saying I did a lot of Excel spreadsheets. I saw my co-workers were using this thing called R. Oh God, that’s so long ago. R and SAS to automate some of the analysis we were doing. I’m like, how do I do more of that? I want to be able to control things. And then from there, I moved into data analytics and worked at a few places like Comcast, also working on some of their recommendations and then more into data science.

And when I was doing data science, I saw that people were actually getting data programmatically themselves. And I’m like, okay, I can analyze the data. How do I actually get that data? So that’s when I moved more into machine learning engineering, which is the process of synthesizing the data, being able to ship it. And then I saw that people were not only building models, but they were shipping it to production. I’m like, how do I do more of that?

So my whole career has been about moving towards doing more and more and more of that process myself. And I think that’s pretty reflective of where the industry has been headed in general, right? So we started out with data science or with analytics, with bespoke packages, bespoke models. And now we’re at the point where we’re running enormous clusters that are running LLMs. And now we have inference engineering as the hottest topic.

So I think my career fairly mirrors that. So like I said, I’ve worked at Comcast. I got into the recommendations and search space completely by accident, I think, as a lot of people do. I love it. I love working on search and recommendations.

I worked on recommendations for Tumblr, which is an old school social network. Then I worked on ML platforms at Duo. I worked on LLMs at Mozilla.ai. And right now I work at Malachyte, which is a startup based in New York doing search and recommendations and real time personalization for e-commerce. So, it kind of echoes of the same thing that I’ve been doing.

(Yacine) But for e-commerce merchants specifically and personalizing their stuff in real time. So cool. And I feel like every time I ask somebody, like how they got started with them, they never, it’s very rare that somebody went to, oh yeah, I did a bachelor in machine learning. Like they usually come from all over the place. In my case, I’m from neuroscience. So like I had to pick up like machine learning because I was like, how the hell I’m going to do like all these stats on like these data sets. There’s just no way. And then they were so like complex that I had to programmatically like interact with them in some shape or form. And I had to run them on the Slurm, with Slurm on the cluster. So I had no choice, right?

Otherwise, like I’m not going to get the work done. But then you start to, I don’t know, I feel like once you start to kind of realize what you can do with it, it’s kind of like something like a facet of programming that just appear, right? A bit like before you know programming, you’re like, you don’t know, like that you can automate stuff deterministically, then you learn maybe about like functional programming, like object oriented, there’s different ways of thinking about that. But then you learn about like machine learning, where you have like this black box, and then you can feed it data, and then the output that you want, and it will automate this without you having to like define it. So do you feel like machine learning is more of a set of tools and skills than like a career, per se?

(Vicki) It certainly seems like it’s becoming more of that. I think like we have a lot more blending, because machine learning used to be, like you said, very much a black box, right? It just kind of sits over there. And there’s even a paper about it, which is not machine learning is the high interest credit card of technical debt. But there’s another one that says like the model is the black box. And basically all the parts of the machine learning system are around it. And you don’t know what’s in it. But now what’s happened is like everybody has access to this model, right? Everybody has access to the API.

And so we don’t necessarily have access to what’s inside of it. But at least more of that has been exposed and is now being synthesized in products a lot, a lot more than it used to be. So it definitely feels like it’s become more of a tool that you need to learn. At the same time, like with my own biases of working with a lot of these larger systems, is that these systems also themselves have gotten bigger and have integrated more of like, allows in those traditional tools.

(Yacine) And so you still really need to know what you’re doing with machine learning to figure out where you want to incorporate that. This is good. And I think the big question I want to ask here, and I have a whole bunch of others, but like the big question is like, what do you think like, in 2026, someone that wants to learn like mastering or deep learning should be doing right? And let’s say it was you, like, back in the days, and now you realize that there’s this whole thing, what would you do?

(Vicki) Yeah, this is a big caveat. Because the thing with people who are kind of like in the industry is they don’t remember what was hard. So I’m thinking back to like myself, when I was starting, we might get into this a little bit later, using Git. It’s like, oh, yeah, just now you just use Git. But I remember the first time I used it, I wanted to cry. I’m like, Oh, my god, why do we need this? Like, it’s so hard.

Why do we do this stuff? And then just when I would ask somebody to like, for example, I needed to run the report, they would be like, Yeah, you do these three things. And then you start running the report. And you’re like, Oh, okay, you need to connect to the database. Oh, you need to shell into this thing. There’s all this other stuff that they forget, because it’s just so intuitive to them after doing it a while. And I feel like that’s true with, with starting out too. So that’s my caveat for what I would do, because I don’t know, but I was looking at, I put together a gist, it’s called normcore LLM reads.

And it’s called like the anti-hype LLM reading list. I put this together, I think, in 2023. It was just when ChatGPT came out, and everybody’s like, Whoa. And I think everybody in machine learning was also like, Whoa, even though we’ve been working on this stuff, this kind of came out of left field. So I took a look at it. And I’m like, Well, there’s a lot of building block components that go into this. It’s not like ChatGPT just emerged out of thin air. And so at that time, I put together a reading list that had things like word to back transformers and support vector machines, like deep learning systems, fundamental ML readings, linear algebra, all of this stuff. And I’m like, what would I update this list to now, it would still be the same.

Like, I think that if even if most of your work is with APIs, it is empowering to understand what’s going on behind the API. With local models, you can just take it all apart and look at every single thing, look at the stack trace, etc. But even if you’re working with these other models, and especially if you want to evaluate like stuff like Jev, or whatever that just came out, you need to have the context of like, this is where it fits into machine learning.

Like, it’s not whoa, Jev came out of nowhere. It’s like, okay, you know, we’ve been doing prediction and classification for a long time. Like, that’s what machine learning is, right? prediction, compression, all those things.

And I think I would learn the same things like do the books, do the old school readings, like build nano GPT, all that stuff, because what it does is it grounds you, it says, okay, this is not entirely new. We’ve done, you know, history doesn’t repeat, but it rhymes. We’ve done this before. I’ve seen this pattern before. key value, cash is just a hash map, or it’s just the dictionary, or it’s just like Redis, I actually gave a talk about this once.

And once you do that, you start to learn patterns, and you become a lot more powerful. So my TL; DR is I would still learn all the same stuff. And moreover, I would even start to learn from books, there’s something about like reading text for humans. I know a lot of people have a lot of different ways that they consume information. But there’s something that like when you highlight when you mark it up, when you do the equations yourself, you really start to soak it in a lot more than you would. The one thing that can help now is if you’re confused, you can ask, right? You can be you can ask, I’m confused about this, explain it to me like I’m fine. I still don’t understand it. Give me a concrete example. Outside of Perplexity, like within harnesses, we don’t utilize links to web search as much as we should. So we have this thing that can augment us.

But if you want to learn machine learning, like most of the pain should be on your side, because you should be learning, not the machine. And then you can power the machine.

(Yacine) I think that’s, that’s so true that like, you have like, especially if you’re starting out and you don’t understand the concepts. I don’t