Zeer
MLCommonsMLCommons buying intent
11 tracked signals — top 9 topics below — Engineering is carrying most of it.
Attention by team
taxonomy_intent_rollup × social_profile.roleEvery tracked signal from a MLCommons employee, placed by the team they sit in and the theme they engaged with. Darker means more concentrated attention.
Topics being researched
30-day windowAll tracked topics, ranked by signal volume. Confidence is the classifier's certainty that the signal belongs to this topic.
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Need intent for a specific topic or industry?
We track the full taxonomy across every account in the graph — including themes not shown on this page.
Who to contact at MLCommons
verified title on filePeople at MLCommons whose own activity produced these signals. Names are withheld pending a consent decision; titles, seniority and topic are real and free to browse.
Top accounts researching MLCommons
names withheld on the public pageCompanies whose people mention MLCommons in their own activity. Account names are withheld here; not yet classified as implementation partner vs. genuine prospective buyer.
No buyer signal yet for this account
Nobody in the graph is currently discussing this company by name in a way we can attribute to a specific employer.
Buyer profile
company size · seniorityHow big those accounts are, and who inside them is senior enough to matter. Competitor products still not yet computed for this account.
What's been said
public posts mentioning MLCommonsReal public activity that surfaced MLCommons in a tracked topic. Not a sentiment score — just what people actually wrote.
Some notes on what I think *might* be worth getting good when many people are asking - “what’s worth learning?” “what is worth creating?” “what would schools teach?”, with no guarantees on whether these may be right and recognizing that the paths to career and educational “success” are subjective and meandering with no right answers. Plus some readings on AI risk measurement, regulations, standards. (Views are personal, link in comments)