ML Engineering is Not What You Think — ML Jobs Explained

Author(s): Boris Meinardus

Originally published on Towards AI.

How much machine learning really is in ML Engineering?

It’s so confusing!

There are so many different data- and machine-learning-related jobs. But what actually are the differences between a Data Engineer, Data Scientist, ML Engineer, Research Engineer, Research Scientist, or an Applied Scientist?!

It cost me way too much time and confusion to figure all this out, so in this blog post, I wanted to do my best to clearly explain what each of those ML jobs means.

So, back when I got my first ML job, I had to work on a lot of the different stages of a classical ML pipeline, starting with data engineering.

Data engineering is the foundation of all ML pipelines. There is literally no reason to even think of ML without thinking of the necessary data.

One of my first tasks was to find one or rather multiple data sources that we could connect to. I then had to build and maintain the whole infrastructure that allows data to flow from our sources to its destination.

This included writing code in Python to automate the data collection process, which returned ugly XML files that then had to be preprocessed to extract all the information that was relevant to us and to have our desired format. The extracted and transformed… Read the full blog for free on Medium.

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Published via Towards AI