In-Person Agenda

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In-Person Agenda

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Monday, August 22

Ray Summit Meetup Community Talks

Tuesday, August 23

Keynotes, Day 1

BigDL 2.0: Seamless Scaling of AI Pipelines from Laptops to Distributed Cluster

Introducing Ray AI Runtime

Running a question-answering system on Ray Serve

Large-scale deep learning training and tuning with Ray at Uber

Reducing cost, latency, and manual efforts in hyperparameter tuning

Zero-copy model loading with Ray and PyTorch

Ray for large-scale time-series forecasting of disk utilization in a data center

The Ray Beam Runner Project: Unified batch, streaming, and ML

Hugging Face + Ray AIR

Distributed training with Ray on Kubernetes

What's new in RLlib

Scaling Instacart fulfillment ML on Ray

Scalable feature engineering with Hamilton on Ray

Multi-model composition with Ray Serve deployment graphs

A serverless resilient graph analysis platform built on Ray

Illuminate your distributed ML workloads with Weights & Biases

FastCFE: A counterfactual explainer using reinforcement learning

Wind farm optimization using RL and large-scale simulation

KubeRay: A Kubernetes Ray clustering solution

Highly available architectures for online serving in Ray

Alpa: Simple large model training and inference on Ray

State of Ray Serve in 2.0

Ray for distributed mixed integer optimization at Dow

Scalable training of language models using Ray, JAX, and TPUv4

Ray Workflow: Durable Ray tasks for efficient, flexible data pipelines

Wednesday, August 24

Keynotes, Day 2

Return of the Redi: Redis and Ray in the ML Ecosystem

Large-scale data shuffle in Ray with Exoshuffle

Gearing up for FIFA 2022 using RLlib-powered traffic control

How Spotify sped up ML research and prototyping with Ray

Scaling Training and Batch Inference: A Deep Dive into AIR's Data Processing Engine

Large-scale distributed training with TorchX and Ray

Paving the Road for Large-Scale Data Processing in Production with Ray

Production RL and decision-making with RLlib

Cruise.data: A new dataset processing pipeline for Cruise ML

The Magic of Merlin: Shopify's new machine learning platform

Large-scale deep learning to augment production RL workloads

Predibase: A low-code deep learning platform built for scale

Operationalizing Ray Serve on Kubernetes

SkyML: Infra-less machine learning on any cloud

Ray + Weights & Biases: Build and deploy real-world ML models

Graphs at scale with Ray, for AI in Manufacturing

A cross-platform, cross-tool Ray on Kubernetes deployment

Evaluating large language models with Ray in hybrid cloud

BigDL 2.0: Seamlessly scaling end-to-end AI pipelines

Ray Observability: Present and future

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