Watching the Tokyo Olympics this summer, especially Anna Kiesenhofer, the Austrian mathematician who won the gold medal in cycling, made me realize that the concept of training is not unique to pro-athletes but also applicable to other professions, such as engineers, musicians, dancers, and so on.
I’m a software engineer and I don’t think we consciously “train” ourselves. However, if you think about it, how we work as engineers is similar to how athletes train. To build something great, like Python, Docker, Linux, etc., or to invent technologies such as Hadoop, CNNs, and GANs, or simply to stay productive as an engineer at tech companies requires years of dedicated training.
Imagine working on a large-scale project; you’d likely commit to it from 9 to 5 for at least six months. If you happen to like the project, you might even work on it for more than 8 hours a day. You’d work on it until late at night and be excited to get up early the next morning to finish where you left off. You would keep thinking about the problem and talk about it whenever you could. If you keep doing this for the next 5 to 10 years, you’ll probably become an expert on that subject.
Even during our “off-season”, we don’t stop training. We deposit like athletes. For example, when engineers are not working on urgent projects, they spend time learning about other relevant technical topics that make them more effective. They want to know what’s popular and make sure their skills are up to date. Machine learning engineers, for example, might explore infrastructure, applied math, statistics, special domain knowledge (e.g., sensors for autonomous vehicles), or even front-end engineering. This off-season preparation builds our muscles, helping us become more effective engineers.
This continuous training might be one reason why individuals from different technical backgrounds sometimes excel in other fields. For instance, some highly productive computer science PhDs I know have backgrounds in disciplines like physics, operations research, or statistics. Their strong foundations enable them to learn core concepts more efficiently, freeing up time to run more experiments and be creative.
Another thing that both athletic performance in sports and technical performance in IT have in common is that it is easy for people to see how well you do.