ExperimentOps: The Missing Layer in Your AI Stack

If you’ve worked in AI development, you know the stack: DevOps for software delivery, MLOps for model lifecycle management. But there’s a gap—a critical layer that most teams are missing.

The Evolution of Ops

Era	Focus	Tools
DevOps	Software delivery	CI/CD, containers, monitoring
MLOps	Model lifecycle	Training pipelines, model registries, feature stores
ExperimentOps	Learning velocity	Experiment tracking, knowledge curation, guided iteration

Why ExperimentOps Matters

1. Knowledge Compounds

Every experiment your team runs generates insights. Without ExperimentOps, these insights live in Slack threads, Notion pages, and people’s heads. When someone leaves, the knowledge leaves with them.

2. Context Beats Benchmarks

Generic benchmarks don’t tell you if a model will work for your users. ExperimentOps lets you define metrics that matter to your business and track them consistently.

3. Iteration Velocity is Everything

The team that learns fastest wins. ExperimentOps removes friction from the experiment cycle so you can test more hypotheses in less time.

The ExperimentOps Workflow

Hypothesis → Design → Execute → Analyze → Learn → Repeat
     ↑                                        |
     └────────── Knowledge Base ←─────────────┘

Each iteration feeds back into your organizational knowledge, making every subsequent experiment more informed.

Implementing ExperimentOps

Start with these principles:

1. Document every experiment - even the failures
2. Define success criteria upfront - before you see results
3. Share learnings broadly - across teams and functions
4. Automate what you can - focus human effort on insights

Ready to add ExperimentOps to your stack? Learn how Marqov can help.