How to Become a Better Machine Learning Engineer
As a machine learning engineer, you know the value of improvements, no matter how small they seem. Here we have a variety of suggestions for improving your performance as a machine learning engineer so you can continually improve yourself and do better work in the field of machine learning.
Love the Steady Approach: Enjoy Iterative Development
Crafting a complete machine learning model feels great, but it does not happen in one attempt, nor does it happen fast. Starting with a very basic foundation and building upon it over many updates and improvements is essential to mastering development processes, and you will be a better machine learning engineer if you learn to like these iterative processes.
Achieving the Apex: Know When to Stop Updating
While it may be good to iterate on your models, there is a point when the updates you program no longer return adequately on your investment in them. You should know when you can stop developing a program, and allow it to operate and breathe for a few moments.
Wanting to Know: Be Curious About Things
Good machine learning engineers look at the world and wonder how things work, and why certain things happen a particular way. This curiosity translates well when programming a machine learning model as good models require the curiosity to know how and why the data changes operations.
Building on Errors: Be Okay with Failure
One of the constants in science is the inevitability and importance of failure. Great lessons can be learned when processes and programs go awry, so learn to be okay with failure. When something goes wrong, look into it, and use that opportunity to push your understanding even further.
Reading Data Accurately: Have Keen Data Intuition
Good machine learning engineers can look at data and read it in ways that don’t make sense to others, and this is a good thing. Work on spotting patterns within data so you can read them and visualize them, then figure out how to model them in your programming language of choice.
Defining Experiment Parameters: Know How to Gauge Good Metrics
Every model and program needs to have a way of measuring success and failure, and this means you need to understand metrics. The ability to set sensible metrics for your machine learning models is crucial to your efficacy, and working on your metrics literacy will only improve your skills as a machine learning engineer.
Addressing Many Issues at Once: Know a Broad Bug Fix
Sometimes, you need to fix several bugs at the same time. It is good to fix single issues, too, but the ability to wrangle several issues together and blast them all away with one update will put you miles ahead of your peers.
Understanding General Users: Put Yourself in Customers’ Shoes
You might know what you want in a device or a program, but you think differently from many people. Instead, imagine the future users of your work, and create something that is pleasing and enjoyable for them.
The life of a machine learning engineer is rife with opportunities for improvement, and if you work on any or all of the areas described above, you’ll be one of the best machine learning engineers out there.
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If you are ready to accelerate your team, then schedule a 10-minute discovery call at https://PROPRIUS.as.me/Discovery. We have a dedicated search process designed to locate, connect with, and deliver the most talented candidates.
If you are looking to propel your career, then schedule a 30-minute intake call at https://PROPRIUS.as.me/Intake. We identify the top Engineers in the Artificial Intelligence Industry that generate results, create opportunity and inspire others to perform their best work.
Joshua Crawford | Managing Director | PROPRIUS
