Within the Creator Highlight collection, TDS Editors chat with members of our neighborhood about their profession path in information science and AI, their writing, and their sources of inspiration. At present, we’re thrilled to share our dialog with Mike Huls.
Mike is a tech lead who works on the intersection of knowledge engineering, AI, and structure, serving to organizations flip advanced information landscapes into dependable, usable techniques. With a robust full-stack background, he designs end-to-end options that stability technical depth with enterprise worth. Alongside shopper work, he builds and shares sensible instruments and insights on information 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 position?
I do, however not within the sense of personally constructing each layer. For me, full-stack means understanding how architectural selections at one layer form system conduct, danger and price over time. That perspective is important when designing techniques that have to survive change.
This attitude additionally influences how I view the info scientist position. Fashions created in notebooks are solely the start. Actual worth emerges when these fashions are embedded in manufacturing techniques with correct information 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 deal with subsequent, and the way have you learnt when a brand new subject is value exploring?
I are inclined to observe recurring friction. After I see a number of groups battle 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 value addressing on the architectural or course of stage.
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 at the moment dealing with or reveals dangers that aren’t but broadly understood. Lastly, I write about subjects I personally discover fascinating and value 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 potentialities. The misunderstanding is that they’re easy. It’s straightforward right now to assemble cloud infrastructure, join an agent framework, and produce one thing that seems to work. That accessibility is effective, nevertheless it masks numerous complexity.
As soon as brokers transfer past demos, the true challenges floor. State administration, permissions, price management, observability, and failure dealing with are sometimes underestimated. With out clear boundaries and possession, brokers turn out to be unpredictable, costly, and dangerous to function. They aren’t simply prompts with instruments; they’re long-lived software program techniques and should 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 information staff trying to keep away from this, what’s your key recommendation on organising an structure?
“The one fixed is change” is a cliché for an excellent cause so optimize for change relatively than for preliminary supply velocity. Even a minimal type of layered pondering helps: separating area logic, utility circulation, and infrastructure considerations.
The aim just isn’t architectural perfection on day one or good categorization. It’s about creating clear boundaries that enable 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 “sooner just isn’t at all times higher.” In a manufacturing ML pipeline, what’s a situation the place you’d intentionally select a slower, safer insertion technique?
When correctness, traceability, and recoverability matter greater than uncooked throughput. In lots of pipelines, decreasing runtime by a couple of seconds affords little profit in comparison with the chance 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 information corruption is way extra expensive than accepting modest efficiency trade-offs, particularly when information turns into a long-term asset others will construct on..
In your Personal, Agentic Assistants article, you constructed a 100% personal, self-hosted platform. Why was avoiding “token prices” and “privateness leaks” extra vital 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 information flows, and exterior dependencies subtly affect how techniques are used and perceived.
I additionally made a acutely aware alternative to not route my private or delicate information by exterior cloud suppliers since there are restricted ensures on how information is dealt with over time. By conserving 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 biggest or most costly mannequin. By decoupling the system from a single supplier, customers can select the mannequin that most closely fits their necessities, balancing functionality, price, and danger.
How do you see the day-to-day work of a knowledge skilled altering in 2026?
Regardless of widespread stereotypes, information and software program engineering are extremely social professions. I strongly consider that probably the most important a part of the work occurs earlier than writing code: aligning with stakeholders, understanding the issue house, and designing options that match current techniques and groups.
This upfront work turns into much more vital as agent-assisted improvement accelerates implementation. With out clear targets, context, and constraints, brokers amplify confusion relatively than productiveness.
In 2026, information professionals will spend extra time shaping techniques, defining boundaries, validating assumptions, and guaranteeing accountable conduct in manufacturing environments.
Wanting forward at the remainder of 2026, what large subjects will outline the yr for information professionals, in your opinion? Why?
Generative AI and agent-based techniques will proceed to develop, however the larger shift is their maturation into first-class manufacturing techniques relatively than experiments.
That transition is determined by reliable, high-quality, accessible information and sturdy engineering practices. In consequence, full-stack pondering and system-level design will turn out to be more and more vital 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 may observe him on TDS or LinkedIn.
