Within the Writer Highlight sequence, TDS Editors chat with members of our group about their profession path in knowledge science and AI, their writing, and their sources of inspiration. Immediately, we’re thrilled to share our dialog with Mike Huls.
Mike is a tech lead who works on the intersection of information engineering, AI, and structure, serving to organizations flip advanced knowledge landscapes into dependable, usable techniques. With a robust full-stack background, he designs end-to-end options that steadiness technical depth with enterprise worth. Alongside shopper work, he builds and shares sensible instruments and insights on knowledge platforms, AI techniques, and scalable architectures.
Do you see your self as a full-stack developer? How does your expertise throughout the entire stack (from frontend to database) change the way you view the info scientist function?
I do, however not within the sense of personally constructing each layer. For me, full-stack means understanding how architectural choices at one layer form system conduct, threat and value over time. That perspective is crucial when designing techniques that must survive change.
This angle additionally influences how I view the info scientist function. Fashions created in notebooks are solely the start. Actual worth emerges when these fashions are embedded in manufacturing techniques with correct knowledge pipelines, APIs, governance, and user-facing interfaces. Knowledge science turns into impactful when it’s handled as a core half of a bigger system, not as an remoted exercise.
You cowl a variety of subjects. How do you determine what to concentrate on subsequent, and the way are you aware when a brand new matter is value exploring?
I are inclined to comply with recurring friction. Once I see a number of groups wrestle with the identical issues, whether or not technical or organizational, I take that as a sign that the problem is structural relatively than particular person, and price addressing on the architectural or course of degree.
I additionally intentionally experiment with new applied sciences, not for novelty, however to grasp their trade-offs. A subject turns into value writing about when it both solves an actual drawback I’m presently dealing with or reveals dangers that aren’t but broadly understood. Lastly, I write about subjects I personally discover attention-grabbing and price exploring, as a result of sustained curiosity is what permits me to go deep.
You’ve written about LangGraph, MCP, and self-hosted brokers. What’s the largest false impression you assume folks have about AI brokers right now?
Brokers are genuinely highly effective and open up new prospects. The misunderstanding is that they’re easy. It’s simple right now to assemble cloud infrastructure, join an agent framework, and produce one thing that seems to work. That accessibility is efficacious, however it masks loads of complexity.
As soon as brokers transfer past demos, the true challenges floor. State administration, permissions, value management, observability, and failure dealing with are sometimes underestimated. With out clear boundaries and possession, brokers turn into unpredictable, costly, and dangerous to function. They aren’t simply prompts with instruments; they’re long-lived software program techniques and must be engineered and operated accordingly.
In your article on Layered Architecture, you point out that including options can usually really feel like “open-heart surgical procedure.” For a newbie or a small knowledge workforce trying to keep away from this, what’s your key recommendation on organising an structure?
“The one fixed is change” is a cliché for a superb purpose so optimize for change relatively than for preliminary supply velocity. Even a minimal type of layered pondering helps: separating area logic, utility circulate, and infrastructure issues.
The objective is just not architectural perfection on day one or excellent categorization. It’s about creating clear boundaries that permit the system to evolve with out fixed rewrites. Small upfront self-discipline pays off considerably as techniques develop.
You’ve benchmarked PostgreSQL insert strategies and famous that “quicker is just not all the time higher.” In a manufacturing ML pipeline, what’s a situation the place you’d intentionally select a slower, safer insertion methodology?
When correctness, traceability, and recoverability matter greater than uncooked throughput. In lots of pipelines, lowering runtime by a couple of seconds affords little profit in comparison with the danger launched by weaker ensures.
For instance, pipelines that feed regulatory reporting, monetary decision-making, or long-lived coaching datasets profit from transactional security and express validation. Silent knowledge corruption is much extra pricey than accepting modest efficiency trade-offs, particularly when knowledge turns into a long-term asset others will construct on..
In your Personal, Agentic Assistants article, you constructed a 100% non-public, self-hosted platform. Why was avoiding “token prices” and “privateness leaks” extra essential to you than utilizing a extra highly effective, cloud-based LLM?
In my each day work I’ve skilled that trusting a system is prime to system adoption. Token prices, opaque knowledge flows, and exterior dependencies subtly affect how techniques are used and perceived.
I additionally made a aware alternative to not route my private or delicate knowledge via exterior cloud suppliers since there are restricted ensures on how knowledge is dealt with over time. By preserving the system self-hosted, I might design an assistant that’s predictable, auditable, and aligned with European privateness expectations. Customers have full management over what the assistant has entry to and this lowers the barrier for utilizing the assistant.
Lastly, not each use case requires the most important or costliest mannequin. By decoupling the system from a single supplier, customers can select the mannequin that most closely fits their necessities, balancing functionality, value, and threat.
How do you see the day-to-day work of a knowledge skilled altering in 2026?
Regardless of frequent stereotypes, knowledge and software program engineering are extremely social professions. I strongly consider that probably the most vital a part of the work occurs earlier than writing code: aligning with stakeholders, understanding the issue area, and designing options that match current techniques and groups.
This upfront work turns into much more essential as agent-assisted growth accelerates implementation. With out clear targets, context, and constraints, brokers amplify confusion relatively than productiveness.
In 2026, knowledge professionals will spend extra time shaping techniques, defining boundaries, validating assumptions, and guaranteeing accountable conduct in manufacturing environments.
Trying forward at the remainder of 2026, what huge subjects will outline the yr for knowledge professionals, in your opinion? Why?
Generative AI and agent-based techniques will proceed to develop, however the greater shift is their maturation into first-class manufacturing techniques relatively than experiments.
That transition will depend on reliable, high-quality, accessible knowledge and sturdy engineering practices. Because of this, full-stack pondering and system-level design will turn into more and more essential for organizations that need to apply AI responsibly and at scale.
To be taught extra about Mike’s work and keep up-to-date together with his newest articles, you possibly can comply with him on TDS or LinkedIn.
