The world’s leading publication for data science, AI, and ML professionals.

The Day To Day Of A Machine Learning Engineer

Act Like The Person You Want To Be

Photo by ThisisEngineering RAEng on Unsplash
Photo by ThisisEngineering RAEng on Unsplash

Act like the person you want to become! When we view ourselves in our mind’s eye, the best thing we can do is imagine ourselves as the future self. The person we want to become. When we imagine the person we want to become as an event that happens in the future, we are subconsciously detaching ourselves from that person. Thus it’s important to picture ourselves as that future person which includes embracing the emotions, feelings, and actions of the future present, even while we remain in the present.

In order to become the person you want to be, You must act like them now.

We must do the tasks they do, make the decisions they would make, and feel how they would feel. It’s for this reason, I’m keen on establishing what the day-to-day of the people in positions I want, looks like. I wake up every day and ask myself – "what would my future self be doing today?". It’s also worth considering what my future self wouldn’t do so that I am able to enter the mindset of the person I want to become.

I’m not going to go too deep into acting like the person you want to become – it’s beyond the scope of this article. But, hopefully, the example above will give you a better insight into understanding why it’s important to understand the day-to-day of a Machine Learning Engineer – if that is what you’d like to become.

A Sample Routine

Of course, routines can vary for different Machine Learning Engineers depending on their organization or personal preferences. The routine I’m suggesting is only a general overview. If your experience is severely different, feel free to leave a message sharing how your day-to-day looks.

Morning

Models that were left running overnight are reviewed first thing in the morning. Machine Learning models may take a long time to train throughout the course of the day which can result in bags of unproductivity as you wait for a result. Consequently, when models are to be trained, this is usually done overnight on the company servers or in the cloud (but this can be risky because costs of using cloud servers may skyrocket), hence the first thought in the morning is to check everything ran smoothly.

This may involve testing the algorithms to ensure they are working as expected, and if they aren’t, understanding what possibly could have gone wrong.

Afterward, you would want to check the tasks required for the day which may consist of team standups, bug fixes, working on a product codebase, or implementing an Algorithm you may want to test in the future.

Afternoon

The morning tasks may take you up to midday. If it doesn’t your next tasks would likely have something to do with programming. Whether it’s conducting analysis or firing up an IDE to write a new class, amend a script, or whatever the case may be.

When the question was posed on Quora, Evan Pete Walsh responded –

"The rest of the day I’m usually head-down coding, either working on a back-end Python application that will supply the AI for one of our products, or implementing a new algorithm that I want to try out."

This seems to be a common theme. The programming work you began in the morning typically takes up a large portion of the day, unless you have meetings or standups scheduled throughout the day. If a model was retrained in the morning, there will be a progress check and evaluation (if the model is finished training) towards the backend of the afternoon. This is typically followed by code reviews.

Late Afternoon

Some organizations allow room for exploration as the day draws to a close. Not all organizations permit this and some prefer to have a designated day of this. Others would much prefer the Machine Learning Engineer to take that initiative upon themselves.

The learning may come in the form of learning from blog posts, reading research papers, watching YouTube tutorials, reading documentation, etc. With the new ideas they’ve learned, they can try implementing them, especially if they believe it would add value to the business.

Final Thoughts

While this guide may be useful to the general population of Machine Learning Engineers, it’s worth conducting your own research to get a better scope of the exact role you’d like to perform. For example, if there’s a company that you know you’d like to work for, begin researching what the day-to-day of someone working there is like. If you can, reach out to someone that works there for a more accurate perspective. Begin carrying yourself as if you work there already.

Thanks for Reading!

If you enjoyed this article, connect with me by subscribing ** to my FRE**E weekly newsletter. Never miss a post I make about Artificial Intelligence, Data Science, and Freelancing.

Related Work

The Difference Between Data Scientists and ML Engineers

How To Become A Machine Learning Engineer

The Best Machine Learning Blogs & Resources To Follow


Related Articles