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UID:pretalx-juliacon-2026-VP8XK9@pretalx.com
DTSTART;TZID=CET:20260814T113000
DTEND;TZID=CET:20260814T114500
DESCRIPTION:This talk discusses our efforts to implement artificial intelli
 gence (AI) workloads on a [**Ray**]([url](https://www.ray.io/#why-ray)) co
 mmodity cluster using the Julia programming language. Similar to Apache Sp
 ark\, Ray is a cluster computing environment for data analytics and AI wor
 kloads\, mainly in Python. First\, we present the configurations and setup
  steps for a Ray cluster. Next\, we discuss the implementation of three di
 stributed clustering algorithms in Ray: **_partition-based_** (a variant o
 f distributed KMeans)\, **_hierarchical_** (the PACk algorithm)\, and **_g
 raph_** (filtered graphs with a distributed hierarchical bubble tree). Spe
 cifically\, we emphasise the integration of Julia and Python within Ray. F
 inally\, we contrast the Ray environment to Apache Spark and highlight the
  lessons learned (limitations and advantages) throughout this experiment.
DTSTAMP:20260502T104551Z
LOCATION:Room 2
SUMMARY:Implementing AI Workloads on Ray in Julia - José Quenum
URL:https://pretalx.com/juliacon-2026/talk/VP8XK9/
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