About Data Learning Science
Skip the theory overload. Dive into practical AI skills that actually matter.
That’s been the promise since day one. It still is.
All learning starts with a data point of one.
Yours. Your context. Your problems. Your industry. That’s where we start.
I’ve been building enterprise systems in production for over 20 years, 6 of those in Applied AI. Not demos. Not pilots that never graduate. Real deployments into regulated industries, with real data, real compliance requirements, and real consequences when something breaks.
This newsletter is where I share what I actually learned. Most of it came from long hours on weekends, through trial and error, building things that had no roadmap and no one to ask.
What you’ll find here
Hands-on technical content, built for people who learn by doing, not by reading slides.
Pattern recognition from production what the textbook says versus what you find when you’re knee-deep in a real deployment.
Honest breakdowns… I write about what went wrong as much as what went right. That’s where the real learning lives.
Tools and frameworks you can apply, not abstract advice, but things you can pick up and use this week.
And a standing question I keep in every conversation: What is most relevant and important to you? Reply to any post. I read everything.
About Mario
I’m Mario Lazo — Principal AI Solution Architect, Graduate Faculty at UT Dallas, and IEEE Co-Chair for GenAI in Central Texas.
I’m the author of AI Data Privacy & Protection (Technics Publications, 2024, preface by Bill Inmon) and have spent 20+ years delivering enterprise Data and AI solutions across healthcare, financial services, and regulated industries.
I’ve shipped AI into production more times than I can count. I’ve also watched it fail — sometimes quietly, sometimes expensively — for reasons that had nothing to do with the model and everything to do with how the organization was built around it.
I’m an accepted speaker at the Toronto Machine Learning Summit in June 2026 and teach “From Vibe Coding to Agent Engineering” at UT Dallas. I organize the GenAI World Austin and Austin MLOps communities.
I write from the field. Not the whiteboard.
A few things to know
Everything here reflects my own experience, analysis, and perspective accumulated over two decades of hands-on work. These are not the views of my employer, clients, or any affiliated organization. I don’t name clients without permission. I write about patterns, not people.
The frameworks I share here are my original work. You’re welcome to cite and reference them with attribution. Commercial use requires a conversation first.
Ready to start?
If you can apply one thing from every post, this newsletter is doing its job.
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All views are my own. No clients or employers are named without permission. Nothing here reflects the positions of my employer or affiliated organizations.
