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Angie Chang: Next up, we have women from MosaicML sharing their unique journeys to machine learning careers. I want to welcome Julie Choi, MosaicML Chief Growth Officer and Laura Florescu, MosaicML, Machine Learning Researcher.
Angie Chang: And they’ll share about how they worked at several tech companies, eight total, including a few unicorns and blue chips, and their reason for joining forces at a new startup focused on making machine learning training better for everyone and please do ask questions of these ladies in the Q&A section of Zoom. They welcome your questions and welcome, Julie and Laura!
Julie Choi: Hi everyone. Let me just pull up this, great hello, Happy International Women’s Day, Laura!
Laura Florescu: Happy International Women’s Day!
Julie Choi: I’m so happy to be here with you in our San Diego offices together in real life. So really, really happy to be here with everyone. Thank you so much. The Girl Geek X organization and Angie and everybody, I know it takes so much work to put this event together and we’re just thrilled to be here today to share from our own career stories, as well as from our current intersection where we’re working at MosaicML to train machine learning models faster.
Julie Choi: So let’s get started with Laura and we’re going to take Q&A at the end. We’ll reserve some time. So Laura, you are a machine learning researcher at MosaicML, and it’s just been a joy and delight to get to know you. Can you tell us more about your path that got you to this point?
Laura Florescu: Yes. Thank you, Julie, would love to. So my journey starts in Bucharest, Romania, where I grew up and went to school. I went to a math and computer science high school, and I guess I just kind of loved math. My father had a deep appreciation for it. And so that wore off a little bit to me.
Laura Florescu: And afterwards I went to Reed College in Oregon when I moved to the United States to study mathematics. And so that’s where my academic roots began. And afterwards for a year I worked at Los Alamos National Lab, where pretty much I learned programming and that’s how I got kind of interested more in engineering and technology.
Laura Florescu: And afterwards I wanted to do my PhD. So I started at New York University and I had the honor and pleasure to write a book with my PhD advisor. And so I got my degree in math, computer science, and afterwards I moved to Silicon Valley where I got interested in AI in startups, entrepreneurship, and I made the decision to join right after a small, at the time, startup called Grok. So they are working on custom hardware for inference in machine learning.
Laura Florescu: So I worked on compilers on machine learning there. I learned a ton and afterwards I went to SambaNova Systems also kind of following my passion of accelerating neural networks training. So SambaNova is also building custom hardware for training neural networks. So I worked on many different areas there as well.
Laura Florescu: And now for about a year, I joined forces with you at MosaicML, again, with the same kind of goal of accelerating AI now through more algorithmic side and system optimizations.
Julie Choi: Amazing. I have one question. I mean, this is a brilliant journey and so many amazing points along the way. How did you decide to go into industry versus academia after your PhD?
Laura Florescu: Yeah. So I think a lot of people finishing their PhDs have that exact dilemma. I definitely did and I think I realized I wanted to have more impact in the world, kind of work on work on something that basically the whole world can benefit from. And I felt Silicon Valley and startups in particular would give me that opportunity to do so.
Julie Choi: So it was about impact?
Laura Florescu: Right. Yeah.
Julie Choi: Great.
Laura Florescu: Yeah. Thank you, Julie.
Julie Choi: Sure.
Laura Florescu: So you are Chief Growth Officer at MosaicML. Can you tell us a little bit about your path and where you have been to get to here?
Julie Choi: I’d love to thank you so much. Yeah. When I was a kid growing up in LA, I didn’t imagine that at this age I would be a Chief Growth Officer. Those jobs didn’t exist back then.
Julie Choi: But I think when I look back on the journey, it kind of makes sense that I’m doing what I’m doing because my job right now is to connect us, right? To build relationships with engineers in the research community, as well as at large or medium or small companies who are looking to build AI. And so I am a connector and I’m a people person, but I am…
Julie Choi: I identify as a nerd. So I started my journey in LA. I grew up as an immigrant. Actually I immigrated to LA from South Korea. My parents moved us here when I was the age of three, and my sister was 0.2, literally just born. And we moved here with kind of everything we had and settled in first El Segundo and then North Torrance, if anyone knows Southern California geography.
Julie Choi: And my parents worked very, very hard. They owned a 7-11 store in Lawndale, close to Inglewood. And so they were very, very, very busy and they basically left my sister and I to kind of figure out what we wanted to do with our spare time. And as many kids during the 80s did, I watched a lot of TV on my own.
Julie Choi: I played video games and I just gravitated towards robots and transformers and robo tech, Voltron, anything mechanical as well as these stories of good versus evil. And I identified with the few female heroes that were in these cartoons. And I guess that kind of just spurred me on towards my path in education.
Julie Choi: I went to MIT, continued to find my people and find my groove. But when I graduated, I didn’t really know what I wanted to do. So I went into consulting. And I started, I spent five years working with fortune 1000 types of enterprise companies, helping them solve problems, primarily in the security domain.
Julie Choi: So I was a hacker, I was hired to penetrate systems. And that was probably the first time I realized what it felt to be the only woman in the room, especially at RSA Conference. Wow. I was the only woman in the room usually and I was just like, wow, okay. But actually even then my team was extremely supportive and I had allies around me and it was like, do whatever it took to make that customer successful.
Julie Choi: And so I moved to Silicon Valley and here we are at MosaicML. I mean the Silicon Valley chapter also intersects with personally a lot of things, right? I met my husband, had my children, settled in where I live now. And at the same time growing in an understanding of what I wanted to do. And most recently, before deciding to go to MosaicML, I was at Intel and at Intel, I spent four very impactful years helping establish the AI business and brand for Intel.
Julie Choi: And actually the last time I gave a talk was Intel at a Girl Geek X conference. So it’s kind of amazing to do this again, about two years later.
Julie Choi: So here we are at MosaicML and we are here and so excited on this journey to accelerate AI development. And we’re doing this kind of differently than anyone else because we’re applying algorithmic research as well as system level optimizations to speed the way neural networks are trained.
Julie Choi: And so what I would love is given your research and engineering expertise, Laura, is if you could talk us through why neural network training is so important.
Laura Florescu: Yeah. Thank you, Julie, of course. So just a very briefly, a little bit about neural networks and why they’re so important and basically why we’re focusing on them. So there’s simply a series of algorithms mimicking the human brain to recognize patterns and relationships in vast amounts of data.
Laura Florescu: And so very briefly in the image below, you can see we have been given a number of images containing the number five and a bunch of neurons that are trained then through providing this kind of data in order to recognize features and textures and patterns in the images in order to correctly identify what the image is.
Laura Florescu: So through such iterations, we learn to classify numbers in this specific example.
Julie Choi: Oh, so this is unstructured data going in kind of like images and speech?
Laura Florescu: Yeah, exactly. So it can be applied to many different fields, basically anything that you humans would, would create, right? So a bunch of images, a lot of language. So you can imagine the whole Wikipedia, the whole internet, right? Speech data.
Laura Florescu: So many, many different fields affecting all of us. And I guess the issue is the training costs for building such powerful large models have spiraled. So they can actually get into the million dollars range for a single run. And in order to build a powerful model, you need several iterations of such training. And so you can imagine quickly getting to tens of millions of dollars.
Julie Choi: Wow. That seems extremely difficult and limiting in terms of who has the capability to train neural networks today. So in general, what are the types of companies that have this capability in house?
Laura Florescu: Right, so those companies would be, Google, Meta, Microsoft who have access to such resources.
Julie Choi: I see. But it feels like for AI to really reach its potential, we need these capabilities to be in the hands of far more than these things.
Laura Florescu: Exactly.
Julie Choi: Enter MosaicML. So Laura, can you tell us about how Mosaic is accelerating the training of these neural nets?
Laura Florescu: Yeah. So that’s exactly where we come in and it’s my passion to work on such problems, especially as they apply to, as we have here, a couple of different tasks, different domains in which we have done research and shown significant progress.
Laura Florescu: So in the area of natural language processing, which encompasses everything from machine translation, everybody speaks different languages. So it’s huge question answering, information retrieval, sentiment analysis for Amazon reviews, for example.
Laura Florescu: So in this kind of area, through the research we have done by combining multiple algorithms, we have shown speedups of up to 3.7x on these GPT type models, which is the state of the art in language models.
Laura Florescu: And in computer vision, so such as classification, what I showed earlier here, you can see a couple of examples in detection and image segmentation, which are crucial for autonomous driving. So similarly through our research, by combining multiple algorithms, we can train such models up to 4.5x faster.
Julie Choi: So if I’m interpreting the speed or the impact of speed, does training 4.5 times faster mean that you can potentially train a model that would’ve taken four weeks in maybe one?
Laura Florescu: Exactly. Yeah. So you can iterate faster and your costs go down significantly.
Julie Choi: Awesome.
Laura Florescu: What’s really good about it in my opinion, another thing that we’re doing at Mosaic is we have open source our library of such algorithms. So you can visit it on GitHub, it’s called Composer. So it’s a flexible system to combine efficiently such different algorithms.
Laura Florescu: There are about 20 of them right now, and we’re actively researching and implementing more. And yeah, so we opensource that. We welcome community interaction, community feedback, as well as contributions to our open source library.
Julie Choi: And so is this available today for developer use?
Laura Florescu: Right. And that’s exactly how we got the kind of results that I just described.
Julie Choi: The 4x speed up on vision and four and a half… Okay, perfect.
Laura Florescu: Yeah. So my question to you, Julie, then is we have seen obviously how ML is so important and it’s affecting our lives, but why work in it? What’s in it for us?
Julie Choi: Yeah. So why work in ML? I’ve been working in ML for the past seven years. So I started working in machine learning at HPE, and then I went to Intel and I continued to choose to work in this domain because whether we’re ready to embrace it or not, the era of AI is happening now. I mean, it is not a future thing.
Julie Choi: There is so much data that we’re generating every day on our mobile devices and through our computers that now any company in it, not only the things, but there’s thousands of enterprise companies with legacy data and new data being generated, any organization can create AI systems.
Julie Choi: And so the era of AI is upon us because of the convergence of data, as well as tools that extract meaning from the data. And so I feel like it’s very imperative for me to be a part of developing tools that accelerate this adoption, because at the end of the day, AI systems are acting on my behalf.
Julie Choi: They are identifying who I am, right? And they are trying to make decisions on my behalf. And so I would like to be part of setting up the requirements for AIs, both from the ground up at the tooling level, which is where we’re involved as MosaicML and help educate builders of a AI applications so that we can consider basically people like me, right?
Julie Choi: And today is International Women’s Day. And basically almost 50% of the world identifies as female and that’s about 4 billion people. However, only 15% of the ML space in terms of research and science and development identifies as females. And so this is part of why I choose to work in this domain.
Julie Choi: And so actually, if that resonates and if what Laura, you and I discussed resonates with people that are attending the conference today, I really encourage you to join us here at Mosaic.
Julie Choi: It is an incredibly exciting time to be working on machine learning infrastructure and algorithmic software and to be shaping the space and the opportunity that AI presents. So I would like to just, maybe now we can move into question and answer, we’ll stop sharing, and then let’s go into Q and A. So there are a few.
Laura Florescu: Julie, I have a question for you.
Julie Choi: Yes.
Laura Florescu: Do you have any recommendation to someone who might not have any AI or ML background in order to get into the field?
Julie Choi: Yeah. I mean, I think education, there are so many materials out there, on Coursera, as well as there’s many organizations like Women in ML, Women in Data Science, these types of organizations.
Julie Choi: I would definitely go and look for the coursework, if you’re looking for a technical background and then just talk to people, right? Whether it’s over Zoom or now over coffee, learn from the practitioners who are out there.
Julie Choi: Again, I’ve been in this for seven years and so we’ve kind of come to a state where there are lots of sources of information. Yeah. It looks like, oh, I’m so sorry. There’s a lot of, I think we have a couple more minutes here.
Angie Chang: We’re actually out of time, but if you’ll hop into the chat, we can have you answer questions.
Julie Choi: Okay.
Angie Chang: Thank you Julie And Laura for sharing about machine learning careers and how MosaicML is making machine learning training better for everybody.
Like what you see here? Our mission-aligned Girl Geek X partners are hiring!
- Check out open jobs at our trusted partner companies.
- Watch all Elevate 2022 conference video replays!
- Does your company want to sponsor a Girl Geek Dinner? Talk to us!
