Zeer
ZenMLZenML
Buying intent
9 tracked signals | Top 7 topics are below.
Attention by team
LinkedIn activity, by teamWhere ZenML's own people are actually spending their attention, by team, by topic. Bands run Low to High against the busiest pairing on this page, and each cell also shows how much of that team's own activity it represents.
Topics being researched
30-day windowEvery tracked topic, ranked by volume, not by our guess at what matters. Confidence is the classifier's own certainty that a signal belongs where we've filed it.
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We track the full taxonomy across every account in the graph — including themes not shown on this page.
Who's active at ZenML
verified title on fileTitles, seniority and topic straight from each person's own activity, with a LinkedIn link so you can check any of them yourself.
Primary products / business lines
LinkedIn company profileZenML is an extensible, open-source MLOps framework for creating portable, production-ready MLOps pipelines. It\'s built for data scientists, ML Engineers, and MLOps Developers to collaborate as they develop to production. ZenML has simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows. ZenML brings together all your favorite to
Top accounts researching ZenML
names withheld on the public pageThese are companies whose own people brought up ZenML unprompted, not accounts we guessed might be interested. We can't yet tell an implementation partner from a genuine buyer here, names unlock along with the buyer profile below.
151,426 companies · 839,012 people are researching Artificial Intelligence
ZenML's own team shows 3 signals on this topic. No one outside ZenML has been seen researching the company by name yet — so this is the market it sits in, not a list of its buyers.
- Siemens180 cos · 1,070 people
- Kubernetes4,474 cos · 15,721 people
Buyer profile
company size · seniorityCompany size and how senior the people involved are, the two things that decide whether this is a real deal. Competitor overlap isn't computed yet for this account.
Buying committee functions
Employee job titles (LinkedIn)Leadership — 1 person
What's been said
public posts by ZenML's teamNo public post naming ZenML has surfaced in the past year, so this is what ZenML's own team is posting about publicly — their topics, in their words.
The only thing we have which is closest is a trace, but it is actually very disconnected from the runtime in which these agents actually execute.
View source postRecorded this with Demetrios in a random hotel in Augsburg talking about long-running durable agents. 🍺 may or may not have been involved! Weekend listen for those who are interested in architectures for durable execution ;-) Link in comments!
May 2026A scenario from the trenches: your agent runs for 6 hours and costs $40. The output is fine. You suspect a cheaper model would have produced something nearly as good. What can you actually do with this information today? Observability tools, and the good ones are great, can show you the spike, group by model, dig through the trace. What they can't do is take that exact run and re-execute it with the cheaper model to find out if the output is still good. Observability watches. Runtimes act. A durable agent runtime should let you fork any production run with one variable changed (model, context size, prompt, skill) and get back a side-by-side comparison. Keep the variant that's still good enough. Ship that. The reason observability tools can't do this isn't laziness... they don't execute anything. To replay a run with overridden inputs you need durable checkpoints, artifact lineage tying the variant back to its parent, and flow versioning so both are addressable. Different category, different tools. I think this is what the open agent runtime layer is for.
May 2026