How To Stay Relevant As A Machine Learning Engineer In 2021
By: Richmond Alake
Tags: Machine Learning, Data Science, Technology, Careers, Artificial Intelligence

Have A Consistent Learning Routine

Success isn’t always about greatness. It’s about consistency. Consistent hard work leads to success. Greatness will come — Dwayne Johnson

One of the main lessons I learned in 2020 is that nothing beats consistency, not talent and not even luck.

Writing over 150+ Medium articles in 2020, and publishing at least 3 AI/ML/DS articles a week over the year made me realise that if you stay consistent, it is possible to accumulate invaluable experience that produces results that can’t be replicated through luck.

In past articles, I’ve alluded to some of the reasons I write on Medium and also the benefits realised as a Machine Learning Practitioner. Through the creation of a regular writing and learning routine, I’ve been able to deliver content on the Medium platform continuously.

This brings us to the first strategy I’ll be incorporating in 2021 to stay relevant within the ML industry. My plan for staying relevant is to transfer the notion of consistency and regime to my accumulation of ML related knowledge.

As a Machine Learning professional, you come across novel algorithms, models and techniques every day.

Most times, to leverage these new algorithms and techniques, you are forced to learn how they operate and function. So, you could argue that ML practitioners are continually learning. However, in 2021, I am making it a habit.

One notable method which I’m using to educate myself and enrich my knowledge in machine learning is to set a measurable goal.

The goal is to read and understand at least 30 deep learning research papers in 2021.

For me, this means reading and understanding the content of a new research paper every two weeks. My approach is to spend at least an hour a day on a research paper. I’m not sure if reading 30 research paper is considered a lot or not, especially for someone more concerned with the engineering of deep learning models as opposed to the study or research.

What I’m sure of is the papers I aim to read are centred around deep learning solutions to computer vision tasks, such as Object Detection, Pose Estimation, Semantic segmentation and more.

Staying Relevant

How will reading research papers help me stay relevant within the ML industry?

In 2020 most of us found ourselves with more time in our hands, typically as a result of the lockdown measures imposed in major cities around the globe. With my time, I explored some of the earliest major convolutional neural networks released by pioneers within the deep learning field.

My exploration of these CNN architectures involved reading research papers, understanding the algorithms and techniques presented and in some cases implementing the CNN architectures using TensorFlow.

Some of the architectures I explored were AlexNet, LeNet, GoogLeNet, PoseNet etc. Transfer learning within machine learning removes the complexity involved in implementing, training and developing conventional CNN architectures.

To gain a deeper understanding of deep learning network architectures, it’s beneficial to visit original research papers and make an attempt to understand the reasoning behind techniques.

Going as far as to implement algorithms and network architecture from scratch will provide any ML practitioner with a more profound understanding of the deep learning domain.

ML practitioners that can read research papers and extract the necessary information to develop an algorithm or neural network architecture are highly sought after across the ML industry.

And this isn't going to change in 2021.