cnvrg.io > 实例探究 > 使用 cnvrg.io 扩展实时机器学习：Playtika 案例研究

使用 cnvrg.io 扩展实时机器学习：Playtika 案例研究

技术

分析与建模 - 机器学习
分析与建模 - 实时分析

适用行业

教育
设备与机械

适用功能

产品研发
质量保证

用例

预测性维护
实时定位系统 (RTLS)

服务

数据科学服务
系统集成

关于客户

Playtika 是一家游戏娱乐公司，连续五年多在畅销游戏方面处于领先地位。他们使用大量数据根据游戏内的操作定制游戏体验。他们拥有超过 1000 万的每日活跃用户，他们需要一个解决方案来扩展实时机器学习流程并处理突发的需求。

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挑战

Playtika 在扩展实时机器学习的生产方面面临挑战。他们现有的解决方案无法处理规模并实时生成预测。他们需要一个能够支持生产模型实时流并与各种流程和系统集成的解决方案。

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解决方案

Playtika 实施了 cnvrg.io AI OS，该操作系统提供具有内置监控和 MLOps 功能的一键式流媒体端点。 cnvrg.io 允许 Playtika 执行实时预测、监控模型并在数据科学家和 ML 数据工程师之间架起桥梁。它还为使用 Apache Kafka 的流端点提供了可扩展的解决方案。

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运营影响

cnvrg.io delivered a simple, and quick solution to Playtika’s unique ML production challenges. It reduced technical debt in Playtika’s workflow, and connected data scientists from engineering teams. cnvrg.io MLOps solutions, enabled Playtika’s engineering team to easily deploy, update and monitor ML in production to ensure peak performance, and reduced complex triggering and scheduling as data came in. Their data scientists are able to visualize results and business impact in real-time in a unified platform. With multiple deployment options, cnvrg.io offered a one click deployment solution which resulted in frictionless deployment solutions for every model in production. cnvrg.io enabled Playtika to instantly deploy with Kafka and easily integrated into their existing system. Playtika’s ML services perform better than ever, with native integration with Kubernetes and Apache Kafka allowing them to successfully handle any spike in incoming demand and predict and handle workloads and scale consistently and linearly by adding more pods.

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数量效益

Increased performance by 40%
Gained up to 50% increase in successful throughput
Reduced latency and error rates to zero

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