Originally published in Towards Data Science, July 6, 2019
Being your own biggest sceptic, the value in trying things which might not work and why communication problems are harder than technical problems.
Machine learning and data science are both broad terms. What one data scientist does can be very different to another. The same goes for a machine learning engineer. What’s common is using the past (data) to understand or predict (build models) the future.
To put the points below in context, I’ll explain what my role was.
We had a small machine learning consulting team. And we did it all, from data collection to manipulation to model building to service deployment in every industry you can think of. So everyone wore many hats.
The past tense is because I’ve since left my role as a machine learning engineer to work on my own business. I made a video about it.
What my day looked like
9am, I’d walk in, say the good mornings, put my food in the fridge, pour a cup of joe and walk over to my desk. Then I’d sit down, look at my notes from the previous day and open up Slack. I’d read the messages and open up any links to papers or blog posts the team had shared, there’d be a few, this field moves fast.
Once the messages were cleared, I’d skim through the papers and blog posts and read the ones which stuck. Usually there was something which may have helped with what I was working on. Reading took up to an hour, sometimes more, depending on what it was.
Why so long?
Reading is the ultimate meta-skill, if there was a better way of doing what I was doing, I could save time and effort by learning it and implementing it.
It’s now 10am.
If there was a deadline approaching, reading would be cut short to push forward on the project(s). That’s where the biggest chunk of the day went. I’d review my work from the previous day and check my notepad for next steps I put down.
About the Author
Daniel Bourke plays at the crossroads of technology, health and art. Daily articles at: www.mrdbourke.com/blog/