course enrollments across three AI courses
Enrollments, not unique learners.
- Scope
- work
- Attribution
- direct
- Period
- 2024–2026 · as of 2026-07-30
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
The public record
Sources, counts, and claims.
Public counts come from the public inventory. Internal outcomes are deliberately rounded, dated, qualified, and separated from revenue or individual-causality claims.
11 public measures · updated July 30, 2026Archive accounting
Two inputs, two different outputs.
Footprint records describe the archive. Impact records support the claim ledger. They are reconciled separately.
Public archive
- 184footprint observations
- 82bodies of work
- 186public forms
- 198evidence records
Impact ledger
- 85impact observations reviewed
- 11rounded public measures
Attribution boundary
What this site does not claim.
- Pipeline supported is not booked revenue or ARR.
- Program reach and organization outcomes are not personal causality.
- Course enrollments are not unique learners or completion counts.
- Same-day account creation is an association, not a conversion rate.
- MCP tool calls describe usage, not users, organizations, or revenue.
Reading rules
Four rules stay visible.
- 01
Count one underlying work once
All 186 public forms remain discoverable. Recordings, translations, mirrors, profiles, excerpts, and evidence pages do not inflate unique-work totals.
- 02
Keep forms attached to their source
A talk, regional appearance, replay, repository, and translation can describe the same body of work without becoming five unrelated accomplishments.
- 03
Separate contribution from causality
Individual roles, program reach, supported pipeline, and organization-wide outcomes are named separately.
- 04
Keep the qualifier beside the number
Every internally derived measure keeps its time window, source class, attribution, rounding, and caveat in the same record.
Claim ledger
11 measures in four contexts.
Learning reach
Enrollment and launch measures from three practical AI courses.
of course enrollments accompanied a W&B account created the same day
An observed same-day association, not a causal conversion claim.
- Scope
- work
- Attribution
- direct
- Period
- 2024–2026 · as of 2026-07-30
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
Evals-course signups in roughly 36 hours
Rounded down from the internal launch checkpoint.
- Scope
- work
- Attribution
- direct
- Period
- ~36 hours · as of 2025-01-10
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
Product adoption
Rounded usage and adoption signals for the W&B MCP Server.
MCP tool calls through July 29, 2026
Rounded down from the July 29 internal analytics checkpoint; direct project usage, not revenue.
- Scope
- work
- Attribution
- direct
- Period
- Through July 29, 2026 · as of 2026-07-29
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
MCP users across 1K+ organizations
Rounded launch-period adoption snapshot.
- Scope
- work
- Attribution
- direct
- Period
- Launch period · as of 2026-06-11
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
of weekly active MCP users were returning users
Weekly active-user composition at the cited checkpoint.
- Scope
- work
- Attribution
- direct
- Period
- Week of 2026-06-07 · as of 2026-06-11
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
of MCP users re-engaged in a week
Rounded weekly re-engagement checkpoint.
- Scope
- work
- Attribution
- direct
- Period
- Week of 2026-07-20 · as of 2026-07-28
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
Program impact
Bounded reach and supported-pipeline measures from public programs.
in supported event pipeline across tracked 2024–2025 programs
Supported pipeline across tracked event programs; not revenue, closed ARR, or personal attribution.
- Scope
- portfolio
- Attribution
- associated
- Period
- 2024–2025 · as of 2025-10-01
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
leads from NVIDIA GTC 2024
Program reach, not individual attribution.
- Scope
- program
- Attribution
- contextual
- Period
- NVIDIA GTC 2024 · as of 2024-04-01
- Source class
- Internal analytics
Public evidenceInternal analytics — rounded public summary
Archive reach
Counts derived only from the normalized public archive.
dated public event and session references
Unique inventory rows; recordings and mirrors are counted separately.
- Scope
- collection
- Attribution
- contextual
- Period
- 2021–2026 · as of 2026-07-30
- Source class
- Public footprint inventory
Public evidencePublic footprint counting methodology
hours of public talks and interviews
Derived from 23 unique recording durations; mirrors and excerpts are excluded.
- Scope
- collection
- Attribution
- contextual
- Period
- 2021–2026 · as of 2026-07-30
- Source class
- Public footprint inventory
Public evidencePublic footprint counting methodology
Public bibliography
198 sources, open to inspect.
198 public sources193—198 shown
- 193Everything You Wanted to Know About LLMOps but Were Afraid to Askvideo · youtube.comSupports Everything You Wanted to Know About LLMOps but Were Afraid to AskOpen ↗
- 194Gradient Dissent Transcriptionsweb page · wandb.aiSupports Gradient Dissent productionOpen ↗
- 195Orchestrating Reproducible AutoML Experiments using PyCaret, W&B, and Prefectweb page · opendatascience.comSupports Orchestrating Reproducible AutoML Experiments using PyCaret, W&B, and PrefectOpen ↗
- 196PyTorch Lightning #19288repository · github.comSupports PyTorch Lightning Fabric integrationOpen ↗
- 197NVIDIA MLOps On-Demand Playlistweb page · nvidia.comSupports LLM Agent Fine-TuningOpen ↗
- 198Prompt Engineering LLMs with LangChain and W&B — Koreanweb page · wandb.aiSupports Prompt Engineering LLMs with LangChain and W&BOpen ↗