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Press Interview

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Weekly Demo

Anyscale's webinars provide Ray users the opportunity to connect and learn from the global community of Ray experts.

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Anyscale Connect

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Anyscale GA Launch

Anyscale Academy provides training on the Ray ecosystem, tailored for data scientists, data engineers, and MLOps engineers.

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Anyscale Academy

Ray Summit brings together the community of architects, machine learning engineers, researchers, and other developers building the next generation of scalable, distributed, high-performance Python and machine learning applications. The conference will include those who are already using Ray platform and those who are interested in learning how Ray can meet their needs.

Ray Summit

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Ray European Meetup Community Talks - February 2023

Arize Joint Webinar

See how Ray and Arize combine to provide highly scalable and easily managed ML deployments, with automatic issue detection and quick troubleshooting. 

Don’t miss this live webinar on Feb, 7, at 10 am PT hosted by Anyscale, the company behind Ray, the unified framework for scalable computing, and Arize, the leader in machine learning observability. This interactive webinar discusses how Ray and Arize combine to provide ease of AI/ML development and observability along with the ability to understand performance, data quality and drift issues. 

Productionizing Machine Learning with Observability, Quality and Flexibility at Scale

Ray January Meetup 2023 

We are delighted to kick off New Year with our first January Ray Meetup with talks from Ray community users and committers. Join us to hear from the Ray team at Anyscale and Shopify about Ray and its usage.

Ray Meetup Community Talks - January 2023

W&B Joint Webinar

Anish Shah

Phi Nguyen

Tricia Fu

Watch this on-demand webinar by Anyscale, the company behind Ray, the unified framework for scalable computing, and Weights & Biases, the leading developer-first MLOps platform. The webinar discusses how Ray and Weights & Biases are used together by developers and AI teams to ease AI/ML development, experimentation, experiment tracking, model scaling and model management. 


Simplify Building, Scaling, Tracking and Monitoring Your AI/ML Models

Use Cases Webinar (1)

Christy Bergman


Watch this on-demand webinar to see examples of how organizations successfully develop and deploy large-scale machine learning applications and address demand forecasting, NLP, scalable reinforcement learning, recommendation systems, estimated on-time arrival, dynamic pricing and more.

See examples of how organizations successfully develop and deploy large-scale machine learning applications using Ray, the Python-native unified distributed framework for AI/ML scaling:

- Scalable demand forecasting and the use of distributed time-series partitions
- Scalable reinforcement learning including use cases in gaming, trading, portfolio optimization, robotics, traffic optimization and more


How AI Teams Ease Scaling Popular AI/ML Use Cases

Robert Nishihara on theCUBE

Watch the interview with Anyscale CEO Robert Nishihara and John Walls from theCUBE as part of their coverage of AWS re:Invent 2022 

TheCube Interviews Anyscale CEO at AWS re:Invent 2022 

November Meetup

Monthly Ray Community Meetup talks for the month of November

Ray Community Meetup Talks - November 2022

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Watch this on-demand webinar to learn how to make scaling ML and Python workloads a simple integrated experience. Watch the new Workspace Environment, powered by Ray, that makes it a simple developer experience to:
- Easily Scale any AI/ML and Python workloads without any complex infrastructure or scaling complexity
- Move and operationalize workloads to production without having to refactor any machine learning code
- Unify the deployment and scaling experience without having to stitch together many different AI/ML tools and frameworks 
- Scale any workload, even on large data, without consuming too much costly compute


Making ML Scaling a Simple Experience for Every Developer

Simplify Scaling Machine Learning

Antoni Baum

Watch this on-demand webinar to learn how to simply scale any AI or Python workload using Ray, the fastest growing unified compute framework for scaling machine learning workloads.

Learn how users are using Ray to build, manage and deploy scalable ML workloads.

See more highlights below: 

Simplify Scaling Machine Learning and Python Workloads

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Monthly Ray Community Meetup talks for the month of September 

Ray Meetup Community Talks - September 2022

Ray Meetup-July 25

Jules Damji

Through a demo using XGBoost for classification, we will demonstrate how you can scale training, hyperparameter tuning, and inference with Ray — all from a single node to a cluster, with tangible improvements in performance.

Ray Meetup @ SF Bay ACM: Scaling ML/AI workloads with the Ray Ecosystem

Ray Meetup-June 22

Gi-Joon Nam

Jinwook Jung

Tomasz Zaluska

It’s time for our June meetup, a monthly series, where we get together to discuss Ray and Ray’s native libraries for scaling machine learning workloads. This month, we have invited Ray community speakers from IBM and Stanford/ETH to share how they use Ray to solve challenging ML problems. Join us to learn about Ray community use cases.

Ray Community Talks: IBM Research and Stanford

A one stop shop for all events, past and future, to connect and learn from the global community of Ray experts.

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From the creators of Ray, Anyscale is a framework for building machine learning applications at any scale originating from the UC Berkeley RISELab.

Scalable AI and Python

Featured Events

Webinar

Simplify Building, Scaling, Tracking and Monitoring Your AI/ML Models

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How AI Teams Ease Scaling Popular AI/ML Use Cases

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TheCube Interviews Anyscale CEO at AWS re:Invent 2022

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Making ML Scaling a Simple Experience for Every Developer

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