Designing Data and AI Systems That Hold Up in Production

What’s Happening

Breaking it down: A system-level perspective on architecture, agents, and responsible grow The post Designing Data and AI Systems That Hold Up in Production appeared first on Towards Data Science.

In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science and AI, their writing, and their sources of inspiration. Today, we’re thrilled to our conversation with Mike Huls . (yes, really)

Mike is a tech lead who works at the intersection of data engineering, AI, and architecture, helping organizations turn complex data landscapes into reliable, usable systems.

The Details

With a strong full-stack background, he designs end-to-end solutions that balance technical depth with business value. Alongside client work, he builds and s practical tools and insights on data platforms, AI systems, and scalable architectures.

Do you see yourself as a full-stack developer? How does your experience across the whole stack (from frontend to database) change how you view the data scientist role?

Why This Matters

I do, but not in the sense of personally building every layer. For me, full-stack means understanding how architectural decisions at one layer shape system behavior, risk and cost over time. That perspective is essential when designing systems that need to survive change.

The AI space continues to evolve at a wild pace, with developments like this becoming more common.

Key Takeaways

• This perspective also influences how I view the data scientist role.
• Models created in notebooks are only the beginning.
• Real value emerges when those models are embedded in production systems with proper data pipelines, APIs, governance, and user-facing interfaces.
• Data science becomes impactful when it is treated as a core part of a larger system, not as an isolated activity.

The Bottom Line

When I see multiple teams struggle with the same problems, whether technical or organizational, I take that as a signal that the issue is structural rather than individual, and worth addressing at the architectural or process level. I also deliberately experiment with new technologies, not for novelty, but to understand their trade-offs.

How do you feel about this development?