Stand Out from the Crowd: Doing Meaningful Work as a Machine Learning Engineer
In the mad rush to master technology and push their products forward, huge tech companies have been hiring many of the most qualified engineers to work in the cutting-edge field of machine learning. This may sound discouraging to aspiring machine learning engineers, but hope is not lost; it is still possible to do great work as a machine learning engineer and avoiding the tech giants grants you a little more freedom than you might think. Here are tips for doing great work and finding purpose as a machine learning engineer.
Knowledge Isn’t Always Happiness: A Doctorate is Not Necessary
While achieving a PhD is a fantastic feat of willpower and knowledge, getting one is not necessary for becoming a great machine learning engineer. The truth is that many doctoral research projects in machine learning focus on improving algorithms, which is great in the long run but not always practical in a business setting. Top researchers and academics are not always the optimal people for business, so don’t fret if you are staring down the road of academia and finding it wanting – or daunting.
Real Results: Focus on a Real Problem and Engineer It
Good machine learning engineers know that the best work comes from a well-defined framework that allows them to gauge their processes and correct them if necessary. This means, ultimately, that solving a real problem is the best way to improve your workflow as a machine learning engineer. Having a defined end-goal helps you set your framework, and from there, you can program a model and watch it respond to data. Tinker and improve it from there, and you’ll be engineering your way toward a real solution to a real-world problem. That’s meaningful.
Do Good Work: Use the Best Data You Can Find
While you work to improve your models and training a machine to perform a specific task, you’ll be inputting all kinds of data. To achieve the best, most accurate, and most workable results, you’ll want to input the best data you can. This means finding accurate pictures or videos if you need them, preferably ones from real life; it means you’ve got to balance out your classes if you’re classifying things. A fair and accurate spread of data improves the accuracy of your program, so feed it the best and most equal portions you can.
Training is Work: Don’t Push for Perfection
Machine learning models are made to respond to data and inputs, and the core of development involves watching how your model responds to new data. Instead of pushing hard for the same, “perfect” results every time, watch for exceptions and study them. Good machine learning engineers find the faults in their programs and figure out how they happened; it is up to you to find them and improve your models.
Work Integrity: Use Good Tools and Good Data
While you work to become the best machine learning engineer you can be, find the most effective tools that work best for you. By combining your knowledge of your tools with the cleanest data you can find, you will practice and continually improve your skills as a machine learning engineer. That’s what meaningful work is all about: learning and improving.
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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.
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Joshua Crawford | Managing Director | PROPRIUS
