From Data to Deployment: The Evolutionary Path of Machine Learning Engineers

Delving into the Specializations and Expertise Areas in Machine Learning Engineering

Introduction

Data is the new oil, and the AI/ML buzz will not end soon with the rise of LLMs. You might already know that to have accurate ML models, you need clean and well-structured data to do some predictions and training, but serving it to the end users is a bit more complicated than it might look at first glance. One of the roles to make this happen efficiently and scalably is the ML Engineer.

As far as I see, the role of ML Engineer is like all of the data roles — dependent on the company. In some, you might have a narrow responsibilities area; in others, you will have complete end-to-end responsibility. As with all data roles, some specialisations flesh out in larger companies with more strict areas of responsibility. While some of you might disagree with this categorisation, wait for my last post on Data Roles to understand why I’m defining these specialisations and how they fit in the “Great Migration” of Data Folks.

Specialisations

• Generalist: You’re a mix of a Software Engineer, a Data Engineer and a Data Scientist. So you’re covering all grounds…