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Machine Learning Isn’t As Sexy As You Think It Is
Perspective of a ML engineer
Machine Learning (ML) engineers have (unofficially) claimed the crown of being (one of) “The Sexiest Job of the 21st Century”, overtaking the previous champions — Data Scientists.
Even though ML engineers take on a multitude of roles depending on who you ask in which company, a ML engineer’s role still largely revolves around this — deploying ML models to production. While this is what I personally deem as the “sexy” part of the job, ML engineers often have to do many other tasks that are “not-so-sexy”.
Context: I work as a ML engineer on a data science team, with majority of my teammates being Data Scientists and the rest being ML engineers. We manage our own cloud-based infrastructure, but also interface with external teams like Data Engineers, Security Engineers and Site Reliability Engineers.
Our roles are largely pre-defined, with Data Scientists handling data analysis and model development while ML engineers handle model deployment and maintenance.
Of course, every job has its boring aspects. Here are 5 things I do as a ML engineer that are less interesting to me.
