What Is Machine Learning Engineering?

What Is Machine Learning Engineering?

Machine learning engineering is the process of using software engineering principles, and analytical and data science knowledge, and combining both of those in order to take an ML model that’s created and making it available for use by the product or the consumers

For example, a YouTube ML engineer might be in charge of developing the next generation YouTube recommendation algorithm and then developing an ML pipeline around it and integrating it into YouTube such that you, the user, can end up clicking that “next” button to go see that next recommended video.

What does an Machine Learning engineer Do?

they usually are split 50/50: either investing or developing in our ML platform that’s used at SurveyMonkey, and then, on the other hand, assisting and working on further developing our machine learning models at SurveyMonkey.

For example, we recently were able to grow our ML platform capabilities by allowing our models to be automatically retrained.

One of Our favourite machine learning projects that Our worked on was to develop an ML system for a credit union in the area. As part of that project, we wanted to be able to predict which of our users were most likely to open up a credit card account with the credit union.

It was really interesting to deal with financial data since it was a little bit different from the work I had done previously. But it was really enjoyable because it was providing a lot of great value for the credit union directly, and actually also helping their members, who might have benefited from opening a credit card account that they wouldn’t have known to open otherwise.

How did you become an Machine Learning engineer?

In our undergrad, we had studied computer science with a specialization in machine learning and data systems, where we was really able to learn the foundations of what goes into machine learning. But we really hadn’t utilized that in a professional setting yet.

our first job was to work as a data engineering at a really small startup called AirPR. the data pipelines and in data ingestion and flow throughout our entire product. But since we were such a small company of about 25 people, we ended up taking on a lot of different projects that related to ML models and software engineering work.

As a result, we gained a lot of experience in these three domains, and actually those three domains, and that experience in them, has allowed me to become an even better ML engineer today.

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