Chip Huyen, a computer scientist and writer. She went from learning English on her own, to attending Stanford, and then joining NVIDIA and now, SnorkelAI (with plenty of setbacks in between). In this chat, we talked about her love for writing , how it changed her life, as well as her thoughts on machine learning in production.
Interview conducted by Eugene Yan
Eugene Yan (EY): Hi Chip, would you like to introduce yourself?
Chip Huyen (CH): I’m a writer and computer scientist who grew up in a small rice-farming village in Vietnam. I currently work with SnorkelAI, a data-first-end-to-end platform for developing AI applications. What I do is to develop tools and best practices for bringing ML research into production.
One interesting fact about me is that after high school, I went to Brunei for a 3-day vacation, which turned into a 3 year trip through Asia, Africa and South America.
EY: That 3-year round-the-world trek is amazing. How did that come about?
CH: Well, that was actually motivated by failure.
While in high school, I became obsessed with Stanford. People thought I was crazy—I grew up in a small village and didn’t learn English as a child. Even though everyone told me to apply to easier colleges, I had eyes on only Stanford.
So when I got rejected, I didn’t know what to do next. A speaker I met at a conference in Malaysia offered me a job. I took the job and went to visit Brunei for 3 days. There, a lady offered to drive me back to Malaysia and I thought: “Wow, crossing borders is that easy.” Hence, I decided to keep on crossing borders to see how far I could go. That’s how the 3-year trip started.
While traveling in those three years, I wrote a lot. I hosted a newspaper column and published two books. As a result, my English and writing got better. The second time I applied, I was a different candidate. Character development, I suppose. Also, all that writing practice helped me write better admission essays.
EY: You got an internship afterwards with NVIDIA, then joined them full-time. What did you have to demonstrate to get that internship?
CH: First, I have to say that I got a lot of help along the way. I’ve been very lucky in life.
I didn’t have a lot of success. Many of the roles I applied for didn’t work out. I was bad at selling myself. I guess this is part of the Asian culture where we don’t like to market ourselves.
Along the way, I saw an interesting TED talk by Sean Gourley and reached out to him. He told me he had just founded Primer.ai. That was how my second-year internship came about. When I joined, it had 6 people. It has grown since then. (Author’s note: Now, they have about 120 employees and have raised 58 million.)
At Primer, I started to use TensorFlow, but I was struggling to find good resources on it. I asked some of my professors if they could teach a class on it, but they didn’t have the time. They suggested that I teach it instead, as a student-initiated course. That was how CS20: TensorFlow for Deep Learning Research came about. (Author’s note: It’s also known as the Stanford TensorFlow Tutorials.)
That class somehow got popular and NVIDIA and Netflix reached out to me. That was how I got my internship at NVIDIA that eventually turned into a full-time role.
For people who are getting started in their careers, I think they shouldn’t go on the standard path which everyone is on. When you do what everyone else is doing, you’ll have to compete with everyone. When you do your own things, you’ll only have to become better than your past self. So, pick a path that best suits you.
EY: How did the opportunity to join SnorkelAI come about?
CH: One thing I’ve learned is that good startups are always hiring. Startups grow fast and need people.
You need to know what startups are out there. While in stealth, many startups hire from their networks. You want to be plugged into the network to hear about these startups, and for them to know about you.
In addition, when people start something, even if they don’t talk about it directly, they might be talking about things related to it. So you should pay attention to who is talking about the things you’re interested in, try to reach out to them and learn about what they’re doing. Maybe they’ll tell you they’re starting a company.
For SnorkelAI, I read the papers and wanted to learn more. I reached out to the authors. This was how I learned about SnorkelAI and eventually joined them.
EY: Apart from that, you’re also a prolific writer who has written on many useful content, such as your summaries of machine learning conferences and your analysis of tech compensation and ML tools. Why do you write?
CH: I mostly write for myself. I enjoy writing.
My writing usually starts with a question. For example, I was curious about how engineers were paid. This is how the analysis of tech compensation came about. The same goes for the analysis of machine learning tools. I wrote about those to satisfy my curiosity. For the conference summaries (ICLR 2019 and NIPS 2019), I wrote about them as a way to remember better. I know if I don’t write about it, I’m going to forget about it.
Writing has helped me a lot – I would say I would not be half the person today if not for writing. It got me a job, and got me out of poverty. It’s also because of writing that I got accepted at Stanford.
It helps me to learn and organise my thoughts. Whenever I publish something, I get incredible feedback from my audience. Sometimes, this feedback is from people outside of my bubble who have different perspectives—this is very valuable.
EY: You also share a lot about ML in production. What do you think people don’t discuss enough about ML in production?
CH: I think many challenges with ML in production already have solutions in the engineering and DevOps space. Adopting the best practices from engineering should solve 80% of the problems. It’s a number I made up, but you get the point. Maybe people in data and machine learning are unaware of those solutions, or too lazy to learn how to use them.
I think one of the biggest challenges is ensuring that AI is doing more good than evil. Historically, technological advances have allowed those with technology to oppress those who don’t. For example, the industrial revolution allowed many European nations to grow, but it also allowed them to colonize smaller, lesser developed countries. Now, we see that some countries are very good at AI; I’m concerned something similar could happen again.
(Author’s note: Most ML advancements and capabilities lie within a handful of tech giants. I wonder how we can help smaller companies also benefit from machine learning.)
EY: What is your advice for small to medium size companies starting to work on deploying their first machine learning models?
CH: This is a hard question. For me, I like fundamental ideas. I like best practices. There’s a saying I like: “Innovate where you can. Where you can’t, use the industry standards.” For SMEs, instead of chasing fancy new things, I think they should choose less fancy but more stable solutions.
Many enterprises have already seen some benefits from AI. I think it’s important for SMEs to consider ML solutions, and it’s good to start simple.
This article has been edited for clarity and brevity. Read the full interview here.
Eugene Yan works at the intersection of machine learning & product to build ML systems. He’s currently an Applied Scientist at Amazon. Previously, he led the data science team at Lazada and uCare.ai. He writes on how to be effective at data science, machine learning, and career at eugeneyan.com and tweets at @eugeneyan. Follow him at Twitter or subscribe to his weekly newsletter to learn more about this space.
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