从零到无穷大：如何基于AI技术的对冲基金在Kubernetes上构建云原生AI平台 | KC23

From Zero to Infinity: How AI-Powered Hedge Fund Build Cloud-Native AI Platfo... Yang Che & Zhiyi Li

从零到无穷大：如何基于AI技术的对冲基金在Kubernetes上构建云原生AI平台 | From Zero to Infinity：How AI-Powered Hedge Fund Build Cloud-Native AI Platform on Kubernetes - Yang Che, Alibaba Cloud & Zhiyi Li, Metabit Trading

Metabit Trading是一家使用人工智能技术的量化投资公司，他们在K8s上构建了他们的研究平台。然而，他们的计算平台经常面临突然的任务需求，需要从分布式存储系统中并发访问数据的0到数百个pods的扩展。由于这些系统的评级限制和速度较慢，它们显著影响了训练性能并限制了计算可扩展性。为了解决这个问题，他们使用了Fluid和JuiceFS来构建一个弹性分布式缓存解决方案。在本次会议中，Metabit和Fluid的专家将讨论如何通过创建一个弹性缓存集群并使用Prometheus根据缓存使用行为特征制定策略，在生产环境中实现自动扩展，最终达到1000Gbps。他们还将介绍如何使用Fluid和CronHPA进行定时自动缩放，以平衡成本和性能，并评估扩展的性能和成本效益，并展示解决方案的演示。

Metabit Trading is an AI-powered quantitative investment firm that builds their research platform on K8s. However, their computing platform often faces sudden tasks requiring scaling from 0 to hundreds of pods for the concurrent access of data from distributed storage systems. As these systems are rating-limited and slow, they significantly hamper training performance and limit compute scalability. To tackle this, they used Fluid, a CNCF project, and JuiceFS to build an elastic distributed cache solution. In this session, experts from Metabit and Fluid will discuss how to achieve automatic scaling in production environments by creating an elastic cache cluster and using Prometheus to set up a strategy based on behavioral characteristics of cache usage, ultimately reaching 1000Gbps. They will also cover using Fluid with CronHPA for timed autoscaling to balance cost and performance while evaluating the performance and cost benefits of scaling, and present a demo showcasing the solution.

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