Issue #11: Marvelous MLOps

Story of becoming an MLOps tech lead

Before I go into my personal story, I will go through some historical details that made my career path possible.

The emergence of the machine learning engineer

MLOps popularity has grown significantly over the last few years, this can be seen using Google Trends and exploring the number of searches for "MLOps". Interest in MLOps is not surprising: it came from an earlier boom in the data science field that started around 2015.

Many large corporate organisations started investing in data science around that time. Unfortunately, many of the early data science initiatives ended up failing and costing companies a fortune. It turned out, bringing machine learning models to production is much harder than just building a model: data scientists did not have the knowledge of integrating their models with the rest of the company infrastructure, and teams that did have the knowledge, did not know much about machine learning. A lot of organisational changes were required to make it work, and a new profession has emerged: machine learning engineer.

I think, there are two main groups of people that ended up becoming machine learning engineers: a data scientist who learned about software development or a software developer who learned about machine learning. I belong to the first group, and the whole story about the emergence of MLOps feels very personal to me.

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