Becoming A Machine Learning Engineer: Advice From Experts | Software Engineering Perspectives - Built In

We asked three machine learning engineers how they got started in the role and how newcomers can follow in their footsteps by Mae Rice, tech writer for Built In. 
 

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Some datasets escape our understanding. They're vast or complex or both, and we can't analyze them without help. Specifically, help from self-improving machine learning algorithms. 

These algorithms can glean “insights into how the world works that a person wouldn't be able to see, because they're [too] abstract or [too] fine-grained,” says Meghan Hickey, a Boston-based machine learning engineer at Pryon. 

That can mean picking up on patterns humans can’t see — like learning to spot cancer symptoms invisible to the human eye — or performing human analysis at nonhuman speeds...

That isn’t new; machine learning has been around for more than a decade. But interest in the field is skyrocketing lately: search volume for the phrase "machine learning" has roughly doubled since 2016 and machine learning algorithms play an increasingly visible role in everyday tech.

Machine learning engineers play a key role in all this. While they occasionally build machine learning algorithms, they more often integrate those algorithms into existing software. That's done by connecting the algorithms to relevant data pipelines, compressing them so they don’t overload computer systems and enhancing them with intuitive interfaces...
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Source: Built In