Scarce computing resources transformed Carlos Huertas into an optimization master


Watching Iron Man as a college student in his hometown of Tijuana, Mexico, Carlos Huertas was struck by one character in particular: J.A.R.V.I.S., the butler-like artificial assistant embedded in Tony Stark’s armor.

Even though it was only a movie, Huertas knew it foreshadowed real-life potential.

“I was fascinated by that level of technology,” he says.

At the time, he was pursuing a bachelor’s degree in computer engineering at the Universidad Autónoma de Baja California. Inspired by J.A.R.V.I.S.’s impressive communication skills, Huertas decided to pursue a master’s in natural language processing (NLP) at the same university.

That early shift to artificial intelligence ultimately brought him to Amazon, where he is a manager of machine learning on the Buyer Risk Prevention Team in Seattle, which is responsible for protecting customers from fraud and abuse.

Doing more with less

The master’s program was challenging, as NLP requires a lot of hardware horsepower that wasn’t available to Huertas at the time.

Back then, you needed huge machines … We were a very humble facility and had regular consumer computers, so it was hard for me to try to match what people were doing with more resources.

“Back then, you needed huge machines to achieve interesting things, which I didn’t have,” he says. “We were a very humble facility and had regular consumer computers, so it was hard for me to try to match what people were doing with more resources.”

The limited computing resources forced him to think outside of the box and develop creative solutions to do more with less. The challenge energized him, and for his PhD, he turned to the field of machine learning optimization, specifically feature selection for high-dimensional spaces.

That area of machine learning involves designing algorithms that help a machine to focus solely on features that are relevant to a specific task. One example where feature selection may be used is the “cat vs dog” image classification task, a classic machine learning project for beginners that involves classifying photos as containing either a dog or a cat.

Those animals have numerous features, such as color, height, weight, tail, nose shape, and eye color. Humans use their knowledge of the world to understand what helps differentiate them. For example, size might be important as most dogs tend to be bigger, but tail might not be very useful, since both animals have it.

“How do we make sure a machine learns this on its own? Feature selection is the process to help the computer understand that some of the characteristics are more important than others, so it can focus on what matters most and achieve similar or even better level of performance without so much computing power,” Huertas says.

Solving customer problems with machine learning

Huertas routinely applies feature selection in his work at Amazon.

The Buyer Risk Prevention team, Huertas explains, is responsible for keeping Amazon stores safe and trustworthy for customers and selling partners.

“In the spirit of one of our main leadership principles, Customer Obsession, we are constantly innovating and never stop trying to get the best possible experience for all our customers,” he notes. “To this end, we identify pain points and tackle them with technology.”

In order to get it right for customers, in 2019 Amazon created a team to focus on mitigating issues customers might face when reaching out for support with their accounts; that’s the team Huertas currently leads. The team develops machine learning solutions that assist customers in resolving issues with their accounts.

“The algorithm will try to review the case on its own using artificial intelligence and determine the right action for the customer,” he says. “With this, we can provide much faster support.”

As Amazon grows, so too do the amount of data and the complexity of the systems. In that context, it is important to understand which features are relevant to determine whether an issue is legitimate or not.

“This is a perfect match for feature selection, where we ask: ‘Can we be smarter and have a selection of what we should focus on so that our models perform the best without scalability issues?’” he says.

Huertas’ team focuses on providing faster and more accurate responses to customers’ concerns about their account status.

Now, customers who may have encountered issues can reclaim access without having to navigate a complex process. Huertas thinks of his own parents, who are Amazon customers but may have a hard time using third-party systems, such as email, to communicate with Amazon.

Huertas says his background as an assistant professor at Universidad Autónoma de Baja California, where he taught object-oriented programming and web development, helped shape him into a team player and a leader.

“In academia, we have this common phrase that the student doesn’t fail, it is the professor who fails,” Huertas says. “When I was a professor, I felt this need to push my students forward. And that’s something that I still carry with me on my team. I feel a lot of satisfaction seeing my team members develop.”

Discussion grandmaster on Kaggle

Back when Huertas was a PhD student, he joined Kaggle, an online data science and machine learning community. His goal: use the platform to test some of his PhD ideas and see how they fared against real-life problems. Because of his frequent interactions on the platform, where he still serves as a mentor to many of his peers, he holds the title of “discussion grandmaster” and was once one of the five most active users in the forum — among almost 5 million users.

Carlos Huertas (NxGTR) | Kaggle Grandmaster Interview | Kaggle Days

“The community has always been very friendly, and newcomers ask a lot of questions on how to get started,” he says.

At Kaggle, companies promote competitions to solve real-life machine-learning problems.

“It’s especially useful when you’re a student, because in academia you won’t have access to the type of problems that Amazon might have. Getting exposure to those problems without the need to have a job there really helps you to develop your skills,” Huertas says.

In one of those competitions, when Huertas was still a PhD student, he ended up in the top 9 contestants among thousands of scientists around the world. He was competing with a laptop that, he recalls, “could barely run more than a browser.” The experience taught him a lot about how constraints can be empowering.

“It forced me to develop my own packages. And in the process, I learned how things work behind the scenes,” he says. When people have a lot of computing power, he notes, they might forget about the importance of optimization and rely on a lot of pre-built packages that might operate like a black-box.

“When you don’t understand what is the magic happening behind the scenes, it is very hard to progress beyond that,” he says.

His prominence on Kaggle drew interest from ZestFinance, a Los Angeles-based company that offers underwriting analysis for lenders. After a stint building machine learning models for them, he joined Instacart where he helped launch the company’s first customer retention platform by building machine learning models to analyze which customers were more prone to abandon the platform.

Shortly after that Amazon recruiters reached out and he accepted a position on the Buyer Risk Prevention team.

“I like that Amazon puts a huge emphasis on matching your skills with the role,” Huerta says. “While other companies might have generic roles, like data scientist, Amazon has very specialized roles, such as applied scientist, research scientist, data engineer, machine learning engineer. That ensures that you’re going to focus exactly on what you like.”

The advice he provides younger scientists is to always practice what you learn in academia in a real-life setting. He compares it with a sport: You can read several books about soccer, but if you’ve never kicked a ball, it will be very tough to play it.

“It is very important that you materialize that theory into practice,” he says. “If you are still doing your PhD, there are platforms like Kaggle that will provide you with data so that you can practice your skills. By the time you complete your studies, you will have two or three years of technical experience in the field, working with real problems. That will take you very far.”

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