Main Purpose
The main purpose of Anyscale is to provide a unified compute platform for AI and Python applications. It aims to simplify the development and production of scalable and distributed machine learning workflows using the Ray project.
Key Features
- Seamless Development and Production: Anyscale offers a unified environment for development and production, allowing users to run, debug, and test their code at scale on the same cluster configuration with the same software dependencies.
- Flexible Dependency Management: Anyscale provides different options for managing dependencies across the cluster, including using existing base images, bringing your own Docker, or utilizing Ray's runtime environments for faster iteration.
- Jobs and Services API and SDK: Anyscale offers an easy interface to operationalize workloads and integrate with existing deployment tools. Anyscale Jobs support cron jobs, ephemeral cluster creation, and retry capabilities, while Anyscale Services provide replica management, no downtime upgrades, and high availability.
Use Case
- Machine Learning Platform Development: Anyscale is used by industry leaders like Uber, OpenAI, Shopify, and Amazon to build their next-generation machine learning platforms using Ray.
- Scalable and Low-Latency Production Services: Anyscale makes it easy to create, deploy, and monitor machine learning workflows in production, whether it's executing a production workflow on a schedule or running highly scalable and low-latency production services.
