Hello, world

A couple months ago I suddenly found myself with a lot of free time on my hands. After taking care of some pending stuff and touching some grass, I decided to stop ignoring what we all know and understand: AI is eating software. It's revolutionizing industries, changing the shape of the world — everything. The interesting stuff, the funding, the innovations, the business cases, the practical day-to-day use cases — it's all happening here. So my curiosity naturally led me to decide to learn more about AI.

Eight years ago, when I started my tech career at Accenture, there was an AI lab that anyone could participate in. From that time I remember the differences between supervised and unsupervised learning, deep Learning terms like CNNs and GANs that I always thought were sexy but never truly understood.

Eight years later, the name of the game is different. It's not about learning linear algebra or calculus (although I definitely want to do that too), or about training models from scratch. It's about understanding how generative AI works — the agents, the LLMs — about being able to work with these models, optimize them, deploy them, monitor them, and in one word, being the person capable of steering them so they give us the results we want.

This is definitely an adventure too interesting to ignore. But where do you even start? Simple. Last week I spent around 40 hours watching and summarizing "AI Engineer Roadmap" videos, reading blogs, podcasts, interviews, and book summaries. Fortunately, the path is straightforward — there's one book that's basically the bible of AI Engineering. Unsurprisingly, that book is called AI Engineering by Chip Huyen. So I subscribed to O'Reilly and started reading it online. That will be my first step, and I will use this blog to update anyone interested about my learning journey.

We will lay our foundation and then, the frontier awaits.