This 3 Step Approach Can Transform Your Data Science Journey
By: Arunn Thevapalan
Tags: Machine Learning, Artificial Intelligence, Education, Data Science, Learning

Finding myself in a challenging situation.

It was a Monday, and we had to do a demo for the stakeholders on a product we have been working on by Wednesday. To give you some context, I was a Machine Learning Engineer who was mostly involved with researching, prototyping, and developing AI products.

We got terrific results for the experiments we ran; the product was in good shape. But we also knew we’d have to build a working application for the demo, and the problem was we were short of time. And on top of it, we didn’t have any experience in building apps.

With the limited time I had, I searched all over google for tutorials, walkthrough videos, documentation, and tools to build machine learning applications. I stumbled upon a tool/library called Streamlit and understood this could do the job.

All of us find ourselves in challenging situations, how we approach them makes all the difference.

Starting to learn all over again.

I love starting to learn all over again. (Photo by Jonathan Borba on Unsplash)

I love to learn all over again. I naturally learn every day. With years of experience, I’ve mastered the art of learning how to learn, and I’m relatively quick about it.

I knew the time I had in hand was limited. Still, I went on to watch walkthrough videos on YouTube, navigate through articles written on Streamlit, and finally, the rabbit hole of official documentation.

I was pleasantly surprised when I discovered how simple it was and built my machine learning app within the day, thereby meeting the Wednesday deadline. A day of learning and developing, boom, we have the working product for the demo!

Learning is truly the first step for mastery, and don’t let anybody tell you otherwise.

Grabbing when an opportunity knocks.

We finally delivered the presentation and demo to the stakeholders. My manager, who was also at the demo, wanted me to introduce this tool to the team.

I didn’t hesitate for a second. I was more than happy to share everything about machine learning app development with the team. I prepared and conducted a workshop, had a live-coding session, and helped the team upskill.

More than anything, I saw it as an opportunity to grow and become better. Post-workshop, I asked for feedback from the team. I listened to what I did well and what I could improve. I couldn’t have been more satisfied; it was a win-win!

Opportunities are everywhere, making it big or letting it go is all on us.

Sharing the experience with the world.

In most of the feedback I received, one thing stood out. My app development process was easy to grasp and saved the team a lot of hours. What if I could share this with the world for everyone’s benefit?

Soon enough, I started writing my heart out. I took the time to create a new real-world example, push the codes to a Github repository, embed code snippets, and finally published it on Towards AI.

Towards AI featured my article on the front page! (Screenshot by Author)

This article went on to one of my most viewed articles. (If you’re curious to read the article, you can do so here.) Everyone who read enjoyed it and my LinkedIn was full of positive feedback. Eventually, Towards AI publication featured my writing for the month's newsletter. I was tasting success—the success of my approach.

And before I could process everything, I became a Top Writer. Here’s a thing about Medium, most Top Writers on medium publish a lot. I don’t. I have hardly 10 articles published. Instead of quantity, I focussed on quality.

I became a Medium Top Writer. (Screenshot by Author)

Every time I publish, I make sure I give something of value to the reader. While delivering value, I focus on improving myself, becoming a better data scientist, and becoming a better machine learning engineer with every article I publish. The top Writer tag means nothing to me; a better data scientist means everything to me.