Distributed PyTorch training Ray Train, Ray Data batch inference, video processing pipeline ML, computer vision fleet management AI, dashcam AI training infrastructure, multi-node GPU training AWS, model testing acceleration, fleet safety AI platform, managed Ray platform, Anyscale vs Kubernetes.

With Anyscale, Motive trains and tests computer vision models on dashcam video from +1 million connected cameras. 

Motive Accelerates Driver Safety

Motive

Motive Accelerates Driver Safety AI on Anyscale

faster model training, from 7 days to 7 hours

faster model testing, from 3 days to 15 minutes

"We shorten our model training from seventy hours to under seven hours, and our testing time from three days to within an hour, and we're seeing significant speed-ups. That's what lets us go from one or two model iterations per release to ten or more."

Tim Cheng

W&B Joint Webinar

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

resource-center-casestudy-samsara

Samsara

Samsara Case Study

Geotab centralizes fleet video AI on Ray and Anyscale, enabling GPU sharing across data science teams and processing hundreds of thousands of videos with on-demand VLM deployment.

With Anyscale, Geotab is able to deliver a unified GPU compute platform where data scientists build models and run inference across billions of video frames to deliver safety and risk reduction insights to drivers and fleet managers.

Geotab enhances commercial fleet safety with video AI on Anyscale

Geotab

Geotab Case Study

higher peak-hour throughput on video processing

“Geotab generates billions of dash cam frames every day, and we need every one of our data scientists to be able to build and deploy AI against that scale. Anyscale's Python and agent native interface to scalable GPU compute lets them do that all without having to be infrastructure experts.”

Mike Wang

Geotab Centralizes Fleet Video AI on Anyscale

Your paragraph text (1)

Access Anyscale today to see how companies using Anyscale and Ray benefit from rapid time-to-market and faster iterations across the entire AI lifecycle.

Modernizing workloads with Gen AI

AI Application Platform Buyers Guide

eBook AI Platform Buyer Guide

resource-center-ebook-ai-platform-buyers-guide

AI Application Platform Buyer’s Guide

Learn how Tripadvisor scaled AI workloads with Ray on Anyscale, reducing large-scale embedding costs by over 80% while accelerating batch and real-time inference across billions of travel signals.

With Anyscale, Tripadvisor modernized its search and recommendation platform with a unified engine for batch and online inference. The platform enables more efficient multimodal embedding processing and lower infrastructure costs across workloads.
<style>
  @media (min-width:1024px){
  .template-case_study  div.overflow-hidden { height:500px;}
  }
</style>

Tripadvisor AI-powered travel journey showing Discovery, Planning and Shopping stages with mobile app screens highlighting AI agents, recommenders, search, computer vision and sorting features

With Anyscale, Tripadvisor modernized its search and recommendation platform with a unified engine for batch and online inference. The platform enables more efficient multimodal embedding processing and lower infrastructure costs across workloads.


Tripadvisor Logo RGB Pine For Light Background

tripadvisor casestudy thumbnail

Any accelerator, any cloud, any framework. Anyscale is made for how you work today and how you want to scale tomorrow.

Tripadvisor powers AI trip planning and semantic search with Anyscale

“By consolidating our workloads on a unified compute engine on Anyscale, we achieved more efficient workload orchestration and up to 80% cost savings with significantly improved GPU utilization”

Jay DeStories

Ray is the leading option for multiomodal data processing and AI model training and services. That's because Ray enables heterogenous compute (CPU-GPU-TPU-Neuron) and fine grained precise orchestration of AI tasks. Apache Spark was built for a structured data and CPU-centric world focused on analytics, BI, and large data processing. 

Ray vs Spark 01

Ray vs Spark

Figure 1: AI computing application scenarios in Weixin

The architecture of a Reinforcement Learning (RL) library is split into two primary components: Generation and Training. During the generation phase, an LLM Engine performs multi-turn rollouts within an environment to produce data and reward signals. This output is then fed into the training phase to update the model's parameters. This process forms a feedback loop, where the progressively improved model generates the next iteration of data for continuous refinement.

Open Source RL Libraries for LLMs Graph

Ebook Landing Page Test

Booklet: Anyscale Product

Anyscale Product

Watch the Webinar

default tile resources bg

Test Webinar Resources

Anyscale use cases

Anyscale Use Cases

Learn more about the Anyscale platform, explore expert insights, and stay on top of the latest trends.


pattren-bg

Powered by Ray, Anyscale empowers AI builders to run and scale all ML and AI workloads on any cloud and on-prem.