After an incredible trip to japan, the summer is flying by!

Lately, my thoughts have been racing. I need to focus on finding ways to apply what I learned during my master’s program so I don’t lose touch with the core concepts and processes. To truly build on that foundation, I know I need to apply my skills to real-world projects that solve tangible problems.

While the data field offers many paths, data visualization has consistently been my strongest area of focus. However, after reading “The Rise of the AI Engineer” by Shawn Wang and reflecting on Andrej Karpathy’s quote that “the hottest new programming language is English”, I’ve grown curious–very curious, actually! I’ve listened to swyx previously on JavaScript and Svelte podcasts, but this framing of the “AI Engineer” role is intriguing to me. It’s not only the role of the AI engineer that is intriguing to me but also how it fits with my background and interests. This is not the role of the Machine Learning Engineer–who builds and trains models from scratch. And neither is it the role of the Vibe Coder–using only prompts to build websites and applications and never looking at the code. It’s something else.

So, driven by that curiosity, I decided to embark on DataCamp’s, Associate AI Engineering for Developers specialization! I completed the first course, “Working with the OpenAI API, largely as a way to “kick the tires” a bit and try to understand what this rapidly evolving field is really about–and whether it might be a realistic direction for me to pursue.

At the same time, I also started LinkedIn Learning’s Fundamentals of AI Engineering: Principles and Practical Applications course by Vinoo Ganesh. He offers a particularly insightful take on how this field requires a fundamental shift in mindset:

“One of the most fundamental shifts you’ll experience as you move into the AI engineering ecosystem is the transition from deterministic to probabilistic systems.”

That statement immediately took me back to my probability coursework in 5130 and the discussion I enjoyed on probabilistic thinking–how uncertainty, likelihood, and approximation fundamentally change how we reason about systems. I even bought and read through the book, “The Drunkard’s Walk: How Randomness Rules our Lives” after taking that class.

So, while I could have spent my summer solely building data visualization projects (primarily in Tableau), I found myself exploring a new direction, once again. Is the AI engineer path a genuine and viable trajectory for me, or am I simply chasing another distraction? That’s the question I’ll be working through this coming year.