Conferences / Ray Summit
Ray Summit 2022
Aug 22–24, 2022 · Hyatt Regency San Francisco (Embarcadero), San Francisco, CA, United States
- Conference
- Ray Summit Vendor & industry
- Dates
- Aug 22, 2022 – Aug 24, 2022
- Venue
- Hyatt Regency San Francisco (Embarcadero)
- Place
- San Francisco, CA, United States
- Format
- Hybrid
- Status
- Past
- Attendance
- —
- Website
- anyscale.com
- Recorded
- 9 sponsors · 84 speaker appearances
- Notes
- Third Ray Summit, first in person: training 22 Aug, general summit 23-24 Aug; free virtual broadcast of keynotes and selected sessions (separate Americas and APJ virtual agendas). Community bash at the Exploratorium. Speakers: full in-person agenda from the 24 Aug 2022 snapshot (training, keynotes, breakouts, lightning talks and the 22 Aug meetup talks); the archived agenda ends at 2:45 PM on day 2, so any later sessions are missing. Media partner logos (unnamed) omitted.
Ticket prices
| Ticket | Price | Valid until | Notes | Source |
|---|---|---|---|---|
| In-person full conference pass (as of Aug 2022) | $100 | — | Reduced price shown in August 2022. | web.archive.org |
| In-person pass including training (as of Aug 2022) | $150 | — | web.archive.org | |
| In-person full conference pass (as of May 2022) | $995 | — | Virtual full conference passes free. | web.archive.org |
Sponsors 9
Title / headline 1
- Anyscale · Presented by
Gold 2
Speakers and talks 84
| Speaker | Company | Role | Talk | Links |
|---|---|---|---|---|
| Anca Dragan | UC Berkeley | Keynote | — | — |
| Bill Chambers | Anyscale | Keynote | — | — |
| Dario Gil | IBM | Keynote | — | — |
| Greg Brockman | OpenAI | Keynote | — | — |
| Ion Stoica | Anyscale | Keynote | — | — |
| Kim Hazelwood | Meta | Keynote | — | — |
| Richard Liaw | Anyscale | Keynote | — | — |
| Robert Nishihara | Anyscale | Keynote | — | — |
| Smitha Shyam | Uber | Keynote | — | — |
| Soumith Chintala | Meta | Keynote | — | — |
| Adam Kelloway | Dow | Speaker | Ray for distributed mixed integer optimization at Dow | — |
| Alexander Sidorov | Cruise | Speaker | Cruise.data: A new dataset processing pipeline for Cruise ML | — |
| Ali Kanso | Microsoft | Speaker | KubeRay: A Kubernetes Ray clustering solution | — |
| Anindya Saha | Lyft | Speaker | Distributed training with Ray on Kubernetes | — |
| Antoni Baum | Anyscale | Speaker | Hugging Face + Ray AIR | — |
| Aparna Dhinakaran | Arize AI | Speaker | Ray Summit Meetup Community Talks | — |
| Archit Kulkarni | Anyscale | Workshop | Machine learning model deployment and serving with Ray Serve | — |
| Arun CS Kumar | Ridecell | Speaker | Reducing cost, latency, and manual efforts in hyperparameter tuning | — |
| Avnish Narayan | Anyscale | Speaker | Production RL and decision-making with RLlib | — |
| Ayhan Demirci | KocDigital | Speaker | Ray for large-scale time-series forecasting of disk utilization in a data center | — |
| Ben Sherman | Weights & Biases | Speaker | Illuminate your distributed ML workloads with Weights & Biases | — |
| Christy Bergman | Anyscale | Workshop | Introduction to reinforcement learning and RLlib | — |
| Clark Zinzow | Anyscale | Speaker | Scaling Training and Batch Inference: A Deep Dive into AIR's Data Processing Engine | — |
| Daniel Acuna | University of Colorado | Speaker | Ray Summit Meetup Community Talks | — |
| David Xia | Spotify | Speaker | How Spotify sped up ML research and prototyping with Ray | — |
| Di Yu | Uber | Speaker | Large-scale deep learning training and tuning with Ray at Uber | — |
| Edward Oakes | Anyscale | Speaker | State of Ray Serve in 2.0 | — |
| Eric Liang | Anyscale | Speaker | Introducing Ray AI Runtime | — |
| Fred Reiss | IBM | Speaker | Zero-copy model loading with Ray and PyTorch | — |
| Guoqiong Song | Intel | Speaker | BigDL 2.0: Seamlessly scaling end-to-end AI pipelines | — |
| Han Li | Instacart | Speaker | Scaling Instacart fulfillment ML on Ray | — |
| Han Wang | Lyft | Speaker | Distributed training with Ray on Kubernetes | — |
| Hao Zhang | UC Berkeley | Speaker | Alpa: Simple large model training and inference on Ray | — |
| Holden Karau | Netflix | Speaker | A cross-platform, cross-tool Ray on Kubernetes deployment | — |
| Isaac Vidas | Shopify | Speaker | The Magic of Merlin: Shopify's new machine learning platform | — |
| Jaehyun Sim | Ikigai Labs | Speaker | Ray Summit Meetup Community Talks | — |
| Ji Lucas | Qatar Computing Research Institute | Speaker | Gearing up for FIFA 2022 using RLlib-powered traffic control | — |
| Jiajun Yao | Anyscale | Speaker | Large-scale data shuffle in Ray with Exoshuffle | — |
| Jiao Wang | Intel | Speaker | BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster | — |
| Jiao Wang | Intel | Speaker | BigDL 2.0: Seamlessly scaling end-to-end AI pipelines | — |
| Jiaxin Shan | ByteDance | Speaker | KubeRay: A Kubernetes Ray clustering solution | — |
| Joanna Yoo | Cohere | Speaker | Scalable training of language models using Ray, JAX, and TPUv4 | — |
| Jules Damji | Anyscale | Workshop | Introduction to Ray for distributed applications | — |
| Jules Damji | Anyscale | Speaker | Ray Summit Meetup Community Talks | — |
| Jun Gong | Anyscale | Speaker | What's new in RLlib | — |
| Karthik Rao | Arthur | Speaker | FastCFE: A counterfactual explainer using reinforcement learning | — |
| Keshi Dai | Spotify | Speaker | How Spotify sped up ML research and prototyping with Ray | — |
| Kuba Perlin | Cohere | Speaker | Scalable training of language models using Ray, JAX, and TPUv4 | — |
| Kyle Bendickson | Tabular | Speaker | A cross-platform, cross-tool Ray on Kubernetes deployment | — |
| Lin Ma | ByteDance | Speaker | A serverless resilient graph analysis platform built on Ray | — |
| Linsong Chu | IBM | Speaker | Evaluating large language models with Ray in hybrid cloud | — |
| Lukas Biewald | Weights & Biases | Speaker | Ray + Weights & Biases: Build and deploy real-world ML models | — |
| Mark Saroufim | Meta | Speaker | Large-scale distributed training with TorchX and Ray | — |
| Mayuresh Kunjir | Qatar Computing Research Institute | Speaker | Gearing up for FIFA 2022 using RLlib-powered traffic control | — |
| Michael Mui | Uber | Speaker | Large-scale deep learning training and tuning with Ray at Uber | — |
| Pablo Estrada | Speaker | The Ray Beam Runner Project: Unified batch, streaming, and ML | — | |
| Paco Nathan | derwen.ai | Speaker | Graphs at scale with Ray, for AI in Manufacturing | — |
| Patrick Ames | Amazon Web Services | Speaker | The Ray Beam Runner Project: Unified batch, streaming, and ML | — |
| Patrick Ames | Amazon Web Services | Speaker | Paving the Road for Large-Scale Data Processing in Production with Ray | — |
| Prashant Kumar | minds.ai | Speaker | Wind farm optimization using RL and large-scale simulation | — |
| Richard Liaw | Anyscale | Speaker | Introducing Ray AI Runtime | — |
| Ricky Xu | Anyscale | Speaker | Ray Observability: Present and future | — |
| Sahil Verma | University of Washington | Speaker | FastCFE: A counterfactual explainer using reinforcement learning | — |
| Sam Partee | Redis | Speaker | Return of the Redi: Redis and Ray in the ML Ecosystem | — |
| SangBin Cho | Anyscale | Speaker | Ray Observability: Present and future | — |
| Shreyas Krishnaswamy | Anyscale | Speaker | Operationalizing Ray Serve on Kubernetes | — |
| Siddhartha Rao Kamalakara | Cohere | Speaker | Scalable training of language models using Ray, JAX, and TPUv4 | — |
| Simon Mo | Anyscale | Speaker | State of Ray Serve in 2.0 | — |
| Simon Mo | Anyscale | Speaker | Highly available architectures for online serving in Ray | — |
| Simon Mo | Anyscale | Workshop | Machine learning model deployment and serving with Ray Serve | — |
| Simon Mo | Anyscale | Speaker | Multi-model composition with Ray Serve deployment graphs | — |
| Siyuan Zhuang | University of California, Berkeley | Speaker | Ray Workflow: Durable Ray tasks for efficient, flexible data pipelines | — |
| Stefan Krawczyk | Stitch Fix | Speaker | Scalable feature engineering with Hamilton on Ray | — |
| Stephanie Wang | Anyscale | Speaker | Large-scale data shuffle in Ray with Exoshuffle | — |
| Sven Mika | Anyscale | Speaker | What's new in RLlib | — |
| Sven Mika | Anyscale | Workshop | Introduction to reinforcement learning and RLlib | — |
| Tobias Wochinger | deepset | Speaker | Running a question-answering system on Ray Serve | — |
| Travis Addair | Predibase Inc | Speaker | Predibase: A low-code deep learning platform built for scale | — |
| Wesley Kerr | Riot Games | Speaker | Large-scale deep learning to augment production RL workloads | — |
| Xu Ning | Uber | Speaker | Large-scale deep learning training and tuning with Ray at Uber | — |
| Xuebin Yan | Cruise | Speaker | Cruise.data: A new dataset processing pipeline for Cruise ML | — |
| Yi Cheng | Anyscale | Speaker | Highly available architectures for online serving in Ray | — |
| Zhanghao Wu | UC Berkeley | Speaker | SkyML: Infra-less machine learning on any cloud | — |
| Zongheng Yang | UC Berkeley | Speaker | SkyML: Infra-less machine learning on any cloud | — |
Sources
- https://web.archive.org/web/20220818205251/https://www.anyscale.com/ray-summit-2022
- https://web.archive.org/web/20220824133853/https://www.anyscale.com/ray-summit-2022/agenda
- https://web.archive.org/web/20220818195901/https://www.anyscale.com/ray-summit-2022/sponsors
- https://web.archive.org/web/20220517081326/https://www.anyscale.com/ray-summit-2022/faq
- https://web.archive.org/web/20220818201842/https://www.anyscale.com/ray-summit-2022/faq
- https://anyscale.com/press/anyscale-announces-ray-summit-2022-the-industry-conference-for-scalable-ai-and-python-applications