Designing Data and AI Systems That Hold Up in Production | Towards Data Science

Towards Data Science

by TDS Editors

February 26, 2026

AI-Generated Deep Dive Summary

Designing data and AI systems that can reliably perform in production environments is a critical challenge for organizations aiming to scale and maintain operational efficiency. Mike Huls, a tech lead at the intersection of data engineering, AI, and architecture, emphasizes the importance of treating data science as an integral part of larger, interconnected systems rather than isolated projects. His approach highlights the need for full-stack understanding—not necessarily building every layer himself—but recognizing how architectural decisions across layers impact long-term system behavior, risk, and cost. This perspective is crucial for creating models that transition effectively from notebooks to production pipelines with proper governance, APIs, and user interfaces. Huls identifies recurring friction points as a key indicator of structural issues worth addressing at the architectural or process level. He encourages exploring new technologies not just for their novelty but to understand their trade-offs and how they solve real problems or reveal hidden risks. His experience has shown that AI agents, while powerful, are often misunderstood due to their perceived simplicity. Once beyond demo stages, challenges like state management, permissions, cost control, observability, and failure handling come into play, requiring robust engineering and operational frameworks. For small teams or beginners looking to avoid the pitfalls of constant system rewrites, Huls advises adopting a minimal form of layered architecture. This involves separating domain logic, application flow, and infrastructure concerns to create clear boundaries that allow systems to evolve without extensive overhauls. His insights underscore the importance of optimizing for change rather than initial delivery speed, ensuring systems remain adaptable and resilient as they grow. In production ML pipelines, Huls notes that faster is not always better. This principle extends to system design, where balancing performance with maintainability is key. By focusing on scalable architectures, proper governance, and long-term operational considerations, organizations can build AI systems that deliver sustained value without becoming unpredictable or costly to manage.

Verticals

aidata-science

Originally published on Towards Data Science on 2/26/2026