Companies
Anyscale
Product category: ML platforms 77% confident
Org type: Software vendor 94% confident
Also listed as 5 other names
Anyscale & UC Berkeley · Anyscale / UC Berkeley · Anyscale Inc · Anyscale.com · anyscale
Conferences sponsored5
Events sponsored9
Speaker slots10755 distinct people
Conferences present at20sponsor or speaker
Years active2020–2026
Talks, past 12 months4#489 on the communication index · many voicesCircuit mix
Where Anyscale shows up, by circuit. Each event counts once: 2 if it sponsored, 1 if it only had speakers. Hover a segment for the conference.
- AI engineering & MLOps38%13 events
- Python & R data science17%6 events
- Modern data stack & practitioner13%6 events
- Cloud providers11%4 events
- Open-source data infrastructure6%3 events
- Software engineering6%3 events
- Vendor flagships & roadshows4%1 event
- Streaming2%1 event
- Alt data & financial information2%1 event
The year around now
Conferences where Anyscale sponsored or had speakers in the past 12 months, and the announced events it is already listed for. One row per conference, January to December; hover a mark for the event and the company's role.
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
Activity by year
Three measures on one year axis; each panel has its own scale.
Conferences present at
Distinct conferences each year, split by whether the company sponsored or only had speakers there.
- Sponsored
- Speakers only
4
2
7
11
4
5
4
Distinct speakers
People from this company on a program that year, each counted once.
4
5
24
36
2
5
5
Sponsorships by tier
Sponsorship rows per year (one per event and listing), stacked by normalized tier.
- Title
- Platinum
- Silver
- Bronze
- Other / unlabeled
2
1
2
1
1
2
Where they've grown or pulled back
FlatSponsored 2 conferences in 2026, flat from 2 in 2023.
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.
Moved up 1
Best tier rose between the first and latest year with a ranked tier.
- PyTorch ConferenceSilver 2024 → Platinum 2026
Once or on and off 2
No clear direction, or a gap we can’t confirm because later sponsor lists are incomplete.
- Data + AI Summitonce, 2023, Bronze
- PyCon (national Python conferences)2 of 4 years, 2020–23
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).
6 Cluster: PyCon · EuroPython circuit →
Often sponsors alongside
Companies that sponsored the same events, by number of shared events (out of 9 this company sponsored).
| Company | Shared events | Share |
|---|---|---|
| Amazon Web Services | 7 | 78% |
| IBM | 7 | 78% |
| 6 | 67% | |
| NVIDIA | 6 | 67% |
| AMD | 5 | 56% |
| Intel | 5 | 56% |
| Meta | 5 | 56% |
| Microsoft | 5 | 56% |
| Arm | 4 | 44% |
| Baseten | 4 | 44% |
| Jane Street | 4 | 44% |
| Red Hat | 4 | 44% |
| Anaconda | 3 | 33% |
| Cloud Native Computing Foundation | 3 | 33% |
| d-Matrix | 3 | 33% |
Neighborhood: companies with nearby conference footprints
Anyscale (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 9
| Event | Date | Place | Tier as listed | Normalized tier |
|---|---|---|---|---|
| PyTorch Conference North America 2026 | Oct 20–21, 2026 | San Jose, United States | Platinum | Platinum |
| Google Cloud Next '26 | Apr 22–24, 2026 | Las Vegas, United States | Explorer | Other |
| PyTorch Conference 2025 | Oct 22–23, 2025 | San Francisco, United States | Silver | Silver |
| PyTorch Conference 2024 | Sep 18–19, 2024 | San Francisco, United States | Silver | Silver |
| Data + AI Summit 2023 | Jun 26–29, 2023 | San Francisco, United States | Bronze Sponsors | Bronze |
| PyCon US 2023 | Apr 19–27, 2023 | Salt Lake City, United States | Partner | Other |
| Ray Summit 2022 | Aug 22–24, 2022 | San Francisco, United States | Presented by | Title |
| Ray Summit 2020 | Sep 30, 2020 – Oct 1, 2020 | — | Host Sponsor | Title |
| PyCon US 2020 (Online) | Apr 15–23, 2020 | — | Silver | Silver |
Speakers 107
| Speaker | Event | Role | Talk | Links |
|---|---|---|---|---|
| Ion Stoica | PyTorch Conference North America 2026 | keynote | Keynote: Ray's Evolution to Support AI Workloads | — |
| Ion Stoica | Ray Summit 2026 | keynote | — | — |
| Keerti Melkote | Ray Summit 2026 | keynote | — | — |
| Robert Nishihara | Ray Summit 2026 | keynote | — | — |
| Suman Debnath | OpenXData 2026 (April) | speaker | The Physics of LLM Inference at Scale | Video |
| Artur Niederfahrenhorst | PyTorch Conference Europe 2026 | speaker | Meet the Ray Maintainers | — |
| Artur Niederfahrenhorst | PyTorch Conference Europe 2026 | keynote | Keynote: vLLM & Ray Updates - Tyler Michael Smith, Chief Architect | — |
| Jaikumar Ganesh | AI By the Bay 2025 | speaker | Scaling Multimodal Data and Reinforcement Learning with Ray | Video |
| Ion Stoica | Ray Summit 2025 | keynote | — | — |
| Keerti Melkote | Ray Summit 2025 | keynote | — | — |
| Robert Nishihara | Ray Summit 2025 | keynote | — | — |
| Robert Nishihara | PyTorch Conference 2025 | speaker | An Open Source Post-Training Stack: Kubernetes + Ray + PyTorch + vLLM | — |
| Robert Nishihara | PyTorch Conference 2025 | keynote | Keynote: Ray: A Distributed Compute Engine for AI | — |
| Jaikumar Ganesh | OpenXData 2025 | speaker | Scaling Multi-modal Data using Ray Data | — |
| Nikita Vemuri | Data Council 2025 | talk | From Scaling to Observability: Solving Key Challenges for Distributed ML with Ray | — |
| Marwan Sarieddine | AWS re:Invent 2024 | breakout | Building your AI stack with MongoDB, Anyscale, Cohere & Fireworks AI (sponsored by MongoDB) | Video |
| Cody Yu | QCon San Francisco 2024 | speaker | Scale Out Batch Inference with Ray | — |
| Marwan Sarieddine | Airflow Summit 2024 | talk | Using the power of Apache Airflow and Ray for Scalable AI deployments | Slides |
| Robert Nishihara | Cassandra Summit 2023 | keynote | How LLMs Have Changed the Way Organizations Do AI | — |
| Ion Stoica | AWS re:Invent 2023 | breakout | Generative AI panel: Moving beyond the hype and realizing value | Video |
| Robert Nishihara | AWS re:Invent 2023 | lightning talk | Secure, private LLMs in your cloud with Anyscale Endpoints (sponsored by Anyscale) | Video |
| Goku Mohandas | 4th Annual MLOps World Conference & Expo 2023 | speaker | End-to-end Production Machine Learning Workflows | — |
| Jules Damji | AI DevWorld 2023 | workshop | Introduction to Ray for Distributed and Machine Learning Applications in Python | — |
| Jules Damji | QCon San Francisco 2023 | speaker | Modern Compute Stack for Scaling Large AI/ML/LLM Workloads | — |
| Ameer Haj Ali | Ray Summit 2023 | speaker | Train and Deploy LLM/Generative AI Application Cheaper, Faster and Safer with Anyscale’s Infrastructure | — |
| Amog Kamsetty | Ray Summit 2023 | speaker | Faster and Cheaper Offline Batch Inference with Ray | — |
| Amog Kamsetty | Ray Summit 2023 | speaker | How Anyscale Speeds up ML Data Processing with Ray | — |
| Archit Kulkarni | Ray Summit 2023 | speaker | KubeRay: A Ray cluster management solution on Kubernetes | — |
| Cade Daniels | Ray Summit 2023 | speaker | Enabling Cost-Efficient LLM Serving with Ray Serve | — |
| Cheng Su | Ray Summit 2023 | speaker | How Anyscale Speeds up ML Data Processing with Ray | — |
| Cindy Zhang | Ray Summit 2023 | speaker | Deploying Many Models Efficiently with Ray Serve | — |
| Edward Oakes | Ray Summit 2023 | speaker | Building Production AI Applications with Ray Serve | — |
| Eric Liang | Ray Summit 2023 | speaker | Ray Data Streaming for Large-Scale ML Training and Inference | — |
| Goku Mohandas | Ray Summit 2023 | speaker | Lessons from Running RAG Applications in Production | — |
| Hao Chen | Ray Summit 2023 | speaker | How Anyscale Speeds up ML Data Processing with Ray | — |
| Ion Stoica | Ray Summit 2023 | keynote | — | — |
| Jiajun Yao | Ray Summit 2023 | speaker | Redesigning Scheduling in Ray to Improve Cost-Efficiency at Scale | — |
| Justin Olsson | Ray Summit 2023 | speaker | Legal Questions Around LLMs and Generative AI | — |
| Justin Yu | Ray Summit 2023 | speaker | Fault Tolerant Training of Large Models: Examples and Tutorial | — |
| Kai-Hsun Chen | Ray Summit 2023 | speaker | KubeRay: A Ray cluster management solution on Kubernetes | — |
| Kourosh Hakhamaneshi | Ray Summit 2023 | speaker | Lessons From Fine-Tuning Llama-2 | — |
| M Waleed Kadous | Ray Summit 2023 | speaker | The New Self-Hosted LLM Tech Stack: Ray, HuggingFace + LangChain | — |
| Philipp Moritz | Ray Summit 2023 | speaker | Lessons from Running RAG Applications in Production | — |
| Ricky Xu | Ray Summit 2023 | speaker | Ray Observability 2.0: How to Debug Your Ray Applications with New Observability Tooling | — |
| Robert Nishihara | Ray Summit 2023 | keynote | — | — |
| SangBin Cho | Ray Summit 2023 | speaker | Ray Observability 2.0: How to Debug Your Ray Applications with New Observability Tooling | — |
| Shreyas Krishnaswamy | Ray Summit 2023 | speaker | Building Production AI Applications with Ray Serve | — |
| Sihan Wang | Ray Summit 2023 | speaker | Deploying Many Models Efficiently with Ray Serve | — |
| Sofian Hnaide | Ray Summit 2023 | speaker | Anyscale Workspaces: A Scalable Interactive ML Development Environment with Zero Setup | — |
| Sriram Sankar | Ray Summit 2023 | speaker | Train and Deploy LLM/Generative AI Application Cheaper, Faster and Safer with Anyscale’s Infrastructure | — |
| Stephanie Wang | Ray Summit 2023 | speaker | The Ray Dataplane: History and the Cloudsort World Record | — |
| Sven Mika | Ray Summit 2023 | speaker | DeepMind’s DreamerV3 on RLlib with multiple GPUs: Out-of-the-box, fast model-based RL. | — |
| Tanmay Chordia | Ray Summit 2023 | speaker | Enabling Cost-Efficient LLM Serving with Ray Serve | — |
| Yi Chang | Ray Summit 2023 | speaker | Ray Scalability Deep Dive: The Journey to Support 4,000 Nodes | — |
| Yifei Feng | Ray Summit 2023 | speaker | Train and Deploy LLM/Generative AI Application Cheaper, Faster and Safer with Anyscale’s Infrastructure | — |
| Yunxuan Xiao | Ray Summit 2023 | speaker | Ray Train: A Library for Seamless and Production-Ready Distributed Deep Learning | — |
| Adam Breindel | SciPy 2023 | tutorial | Scalable machine learning workloads with Ray AI Runtime | — |
| Amog Kamsetty | SciPy 2023 | speaker | Modern compute stack for scaling large AI/ML workloads | — |
| Emmy Li | SciPy 2023 | tutorial | Scalable machine learning workloads with Ray AI Runtime | — |
| Jules S. Damji | SciPy 2023 | speaker | Modern compute stack for scaling large AI/ML workloads | — |
| Jules S. Damji | PyData Seattle 2023 | tutorial | Introduction to Ray for distributed and machine learning applications in Python | Video |
| Kamil Kaczmarek | PyData Seattle 2023 | speaker | Emerging Open Source Tech Stack for Large Language Models (LLMs) with Ray AI Runtime | — |
| Antoni Baum | Data Council Austin '23 | talk | HuggingFace + Ray AIR Integration: A Python Developer’s Guide to Scaling Transformers | — |
| Sharmila Mulligan | AWS re:Invent 2022 | demo theater | Stories from women founders and CxOs | Slides |
| Zhe Zhang | QCon San Francisco 2022 | speaker | Ray: The Next Generation Compute Runtime for ML Applications | — |
| Antoni Baum | Ray Summit 2022 | speaker | Hugging Face + Ray AIR | — |
| Archit Kulkarni | Ray Summit 2022 | workshop | Machine learning model deployment and serving with Ray Serve | — |
| Avnish Narayan | Ray Summit 2022 | speaker | Production RL and decision-making with RLlib | — |
| Bill Chambers | Ray Summit 2022 | keynote | — | — |
| Christy Bergman | Ray Summit 2022 | workshop | Introduction to reinforcement learning and RLlib | — |
| Clark Zinzow | Ray Summit 2022 | speaker | Scaling Training and Batch Inference: A Deep Dive into AIR's Data Processing Engine | — |
| Edward Oakes | Ray Summit 2022 | speaker | State of Ray Serve in 2.0 | — |
| Eric Liang | Ray Summit 2022 | speaker | Introducing Ray AI Runtime | — |
| Ion Stoica | Ray Summit 2022 | keynote | — | — |
| Jiajun Yao | Ray Summit 2022 | speaker | Large-scale data shuffle in Ray with Exoshuffle | — |
| Jules Damji | Ray Summit 2022 | speaker | Ray Summit Meetup Community Talks | — |
| Jules Damji | Ray Summit 2022 | workshop | Introduction to Ray for distributed applications | — |
| Jun Gong | Ray Summit 2022 | speaker | What's new in RLlib | — |
| Richard Liaw | Ray Summit 2022 | speaker | Introducing Ray AI Runtime | — |
| Richard Liaw | Ray Summit 2022 | keynote | — | — |
| Ricky Xu | Ray Summit 2022 | speaker | Ray Observability: Present and future | — |
| Robert Nishihara | Ray Summit 2022 | keynote | — | — |
| SangBin Cho | Ray Summit 2022 | speaker | Ray Observability: Present and future | — |
| Shreyas Krishnaswamy | Ray Summit 2022 | speaker | Operationalizing Ray Serve on Kubernetes | — |
| Simon Mo | Ray Summit 2022 | speaker | Multi-model composition with Ray Serve deployment graphs | — |
| Simon Mo | Ray Summit 2022 | speaker | Highly available architectures for online serving in Ray | — |
| Simon Mo | Ray Summit 2022 | workshop | Machine learning model deployment and serving with Ray Serve | — |
| Simon Mo | Ray Summit 2022 | speaker | State of Ray Serve in 2.0 | — |
| Stephanie Wang | Ray Summit 2022 | speaker | Large-scale data shuffle in Ray with Exoshuffle | — |
| Sven Mika | Ray Summit 2022 | speaker | What's new in RLlib | — |
| Sven Mika | Ray Summit 2022 | workshop | Introduction to reinforcement learning and RLlib | — |
| Yi Cheng | Ray Summit 2022 | speaker | Highly available architectures for online serving in Ray | — |
| Jiajun Yao | Beam Summit 2022 | speaker | The Ray Beam Runner Project: A Vision for Unified Batch, Streaming, and ML | Slides · Video |
| Archit Kulkarni | 3rd Annual MLOps World 2022 | workshop | Introduction to Model Deployment with Ray Serve | — |
| Jules Damji | 3rd Annual MLOps World 2022 | workshop | Introduction to Model Deployment with Ray Serve | — |
| Paige Bailey | DataConnect Conference 2022 | keynote | — | — |
| Jules Damji | Data Council Austin 2022 | talk | Ray: A Framework for Scaling and Distributing Python & ML Applications | — |
| Edward Oakes | Ray Summit 2021 | keynote | Opening Keynote | — |
| Ion Stoica | Ray Summit 2021 | keynote | Opening Keynote | — |
| Robert Nishihara | Ray Summit 2021 | keynote | Opening Keynote | — |
| SangBin Cho | PyCon US 2021 (Online) | speaker | Data Processing on Ray | Video |
| Simon Mo | PyCon US 2021 (Online) | speaker | Patterns of ML Models in Production | Video |
| Edward Oakes | Ray Summit 2020 | keynote | Anyscale Product Demo | — |
| Ion Stoica | Ray Summit 2020 | keynote | Programming the Cloud as Easily as your Laptop | — |
| Robert Nishihara | Ray Summit 2020 | keynote | The Future of Ray | — |
| Dean Wampler | EuroPython 2020 Online | talk | Ray: A System for High-performance, Distributed Python Applications | Slides · Video |
| Dean Wampler | Global STAC Live, Spring 2020 | speaker | Making your analytics more agile | — |