Minimum Viable AI: Redefining How We Build Products

In the startup world, the concept of a Minimum Viable Product (MVP) has been a guiding principle for decades — build the simplest version that delivers value, get it into users’ hands quickly, and iterate based on feedback. Dan Huss believes it’s time to apply the same philosophy to artificial intelligence with Minimum Viable AI.

Rather than pursuing massive, resource-intensive AI initiatives that take years to deliver, Huss argues for Minimum Viable AI — a pragmatic approach that focuses on getting functional, well-governed AI into production quickly. It’s not about building the flashiest model or chasing state-of-the-art benchmarks; it’s about delivering something useful, measurable, and adaptable from day one.

You can listen to the full episode of the ODSC AiX Podcast on Spotify, Apple, and SoundCloud.

What “Minimum Viable AI” Really Means

For Huss, Minimum Viable AI is about starting small but building smart. Just as an MVP should solve a core user problem without unnecessary complexity, an MVA should be the simplest AI solution that works in production — deployed, monitored, and ready to evolve.

This approach forces teams to focus on:

• Clear business objectives — What problem are we solving, and how will we measure success?
• Speed to deployment — How do we get the model into production quickly, without sacrificing quality?
• Governance from the start — Are we ensuring compliance, explainability, and monitoring from day one?
• Iterative improvement — Can we adapt as data changes, regulations evolve, and business needs shift?

Huss sees MVA as a counterweight to the “big-bang” AI projects that often collapse under their own ambition. “We’re seeing new models come out weekly,” he notes. “If you can’t deploy them efficiently, you’re already behind.”

From Marketplace to MLOps: Dan Huss’s Journey

The idea didn’t come out of nowhere. Years ago, Huss built a public marketplace for data scientists and engineers — a place where more than 50,000 practitioners could explore and deploy models. But in working with large enterprises, he discovered a deeper need: the ability to create internal AI catalogs with robust governance and automation.

That pivot led him into the heart of enterprise MLOps, where the focus is on bridging the gap between experimentation and production. Huss realized that an MVA mindset fit perfectly with modern MLOps: instead of trying to perfect models in the lab for months, teams could deploy a working solution quickly, then refine it within a governed, monitored production environment.

How Minimum Viable AI Fits Into Modern MLOps

Huss’s vision for MLOps — developed in part with Gartner — maps neatly to the MVA philosophy. His framework includes:

1. R&D Pipeline — Where ideas are tested in notebooks and training environments.
2. Production Pipeline — A catalog of ready-to-deploy, containerized models for real-time or batch inference.
3. Customization Layer — Feature stores and tools that adapt models to business contexts without retraining from scratch.
4. Orchestration & Monitoring — Systems that keep deployments running smoothly, track performance, and trigger updates when needed.

With Minimum Viable AI, the emphasis is on getting from R&D to production as quickly as possible, then using orchestration and monitoring to guide improvements.

Governance as a Built-In Feature, Not an Afterthought

In heavily regulated industries like finance and healthcare, AI without governance is a non-starter. Huss emphasizes that governance — both data governance and model governance — should be embedded in every stage of the pipeline. This means tracking model lineage, enforcing compliance policies, and ensuring explainability.

Importantly, governance isn’t just about avoiding penalties. Done right, it builds trust with users, customers, and regulators, enabling faster adoption of AI products.

Why Minimum Viable AI Is a Competitive Advantage

Speed has always been a competitive differentiator, but in AI, it’s becoming critical. Foundation models, APIs, and open-source frameworks are evolving so rapidly that a model trained six months ago may already be outdated.

An MVA approach allows organizations to capture value early while staying adaptable. By deploying quickly and iterating often, teams can respond to new opportunities, regulatory changes, and competitive threats without starting from scratch.

“Enterprises want speed,” Huss explains, “but they also want certainty that the model they’re deploying is the right one, with the right controls. That’s what modern MLOps enables.”

Looking Ahead: AI as a Utility

Huss envisions a future where AI operates much like a public utility — reliable, accessible, and governed. But getting there requires a shift in mindset: moving away from AI as one-off projects and toward AI as an operational capability.

Minimum Viable AI is a bridge to that future. By focusing on delivering working, governed AI quickly, enterprises can build the infrastructure, processes, and trust needed for AI to become a seamless part of everyday operations.

Key Takeaways:

• Minimum Viable AI applies the MVP mindset to AI — deliver a functional, governed solution fast, then iterate.
• MVA aligns naturally with modern MLOps, emphasizing speed to deployment, governance, and continuous monitoring.
• Governance is both a regulatory necessity and a trust-building mechanism.
• Speed and adaptability are critical in an era of rapid model evolution.
• Treating AI as an ongoing operational capability unlocks long-term value.