When I think about MLE (Machine Learning Engineer) roles, I often wonder if they’re all about creating new models. I mean, isn’t that what ML engineers do? But the more I learn about the field, the more I realize there’s so much more to it.
Of course, creating new models is a crucial part of the job. You need to develop and train models that can solve real-world problems. But MLE roles also involve deploying those models, maintaining them, and ensuring they continue to perform well over time. It’s not just about building a model and calling it a day.
You also need to collaborate with other teams, like data scientists and product managers, to understand the problem you’re trying to solve and figure out how your model can help. And let’s not forget about the infrastructure side of things – you need to make sure your model can scale and handle large amounts of data.
So, while creating new models is definitely a key aspect of MLE roles, it’s not the only thing. It’s about building a whole system that works together seamlessly.
What do you think? Have you worked in MLE roles or have experience with machine learning projects? I’d love to hear your thoughts on what makes a successful MLE.