Ploomber
Circuit mix
Where Ploomber shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- Python & R data science100%4 events
Presence by conference and year
Each cell is one conference in one year. Fill shows the company sponsored, shaded by its best tier that year; a dot means it had speakers there. Hover for details; click to open the event.
- Title
- Platinum
- Gold
- Silver
- Bronze
- Other / unlabeled tier
- Had speakers
| Conference | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 | 2027 | 2028 |
|---|---|---|---|---|---|---|---|---|---|---|
| PyCon (national Python conferences) | ||||||||||
| PyData | ||||||||||
| SciPy Conference (Scientific Computing with Python) | ||||||||||
| JupyterCon |
Activity by year
Three measures on one year axis; each panel has its own scale. 2027, 2028 is still mostly ahead, so its bars are faded and incomplete.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
Distinct speakers
People from this company on a program that year, each counted once.
Sponsorships by tier
Sponsorship rows per year (one per event and listing), stacked by normalized tier.
- Other / unlabeled
Where they've grown or pulled back
DownNothing recorded in 2026; sponsored 1 conference in 2022.
Counted per conference. 2026 is the latest year whose conferences have mostly happened. Tiers use the best tier each year; swatches show the path, oldest first. A conference counts as pulled out only when a later event published a complete sponsor list, since most events don’t say whether their list is complete.
Once or on and off 1
No clear direction, or a gap we can’t confirm because later sponsor lists are incomplete.
- PyCon (national Python conferences)once, 2022, unranked tier
Lookalikes
Companies with the most similar conference footprint: they sponsor and speak at the same conferences. Bars are cosine similarity, from 0 (nothing shared) to 1 (identical).
- Space Telescope Science Institute0.98
- Streamlit0.93
- Quansight0.92
- NumFOCUS0.92
- The George Washington University0.90
- Python Software Foundation0.89
- OpenTeams0.86
- MLJAR0.86
16 Cluster: PyCon · EuroPython circuit →
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 1 this company sponsored).
| Company | Shared events | Share |
|---|---|---|
| Accounting Data Solutions, LLC | 1 | 100% |
| Activeloop | 1 | 100% |
| AKASA | 1 | 100% |
| Amazon Web Services | 1 | 100% |
| Anaconda | 1 | 100% |
| Ansys | 1 | 100% |
| Anvil | 1 | 100% |
| Auth0 | 1 | 100% |
| BeProud | 1 | 100% |
| Bloomberg | 1 | 100% |
| Bluevine | 1 | 100% |
| Bodo.ai | 1 | 100% |
| Capital One | 1 | 100% |
| Chainguard | 1 | 100% |
| CircleCI | 1 | 100% |
Neighborhood: companies with nearby conference footprints
Ploomber (ringed) and the 30 companies closest to it on the company map. Close together means they sponsor and speak at many of the same conferences. Distances are only meaningful locally; the axes have no meaning. Dot size: conferences present at. Color: cluster.
Sponsorships 1
| Event | Date | Place | Tier as listed | Normalized tier |
|---|---|---|---|---|
| PyCon US 2022 | Apr 27, 2022 – May 3, 2022 | Salt Lake City, United States | Associate | Other |
Speakers 5
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Eduardo Blancas | JupyterCon 2023 | speaker | Beyond Papermill: A New Notebook Executor For Running Notebooks in Production | — |
| Eduardo Blancas | PyData Miami 2022 | speaker | ML without the Ops: Running Experiments at Scale with Ploomber on AWS Batch | — |
| Ido Michael | PyData Miami 2022 | speaker | ML without the Ops: Running Experiments at Scale with Ploomber on AWS Batch | — |
| Eduardo Blancas | SciPy 2022 | tutorial | Ploomber: Developing Maintainable and Reproducible Data Pipelines Interactively From Jupyter, VSCode, and PyCharm | — |
| Ido Michael | SciPy 2022 | tutorial | Ploomber: Developing Maintainable and Reproducible Data Pipelines Interactively From Jupyter, VSCode, and PyCharm | — |