Catwalk的演变:Grab的模型服务平台

As Southeast Asia’s leading super app, Grab serves millions of users across multiple countries every day. Our services range from ride-hailing and food delivery to digital payments and much more. The backbone of our operations? Machine Learning (ML) models. They power our real-time decision-making capabilities, enabling us to provide a seamless and personalised experience to our users. Whether it’s determining the most efficient route for a ride, suggesting a food outlet based on a user’s preference, or detecting fraudulent transactions, ML models are at the forefront.

作为东南亚领先的超级应用,Grab每天为数百万用户提供服务。我们的服务范围从打车和外卖到数字支付等等。我们运营的支柱是什么?机器学习(ML)模型。它们为我们的实时决策能力提供动力,使我们能够为用户提供无缝和个性化的体验。无论是确定乘车的最高效路线,根据用户的偏好推荐食品店,还是检测欺诈交易,ML模型都处于前沿。

However, serving these ML models at Grab’s scale is no small feat. It requires a robust, efficient, and scalable model serving platform, which is where our ML model serving platform, Catwalk, comes in.

然而,在Grab的规模下提供这些机器学习模型并非易事。这需要一个强大、高效、可扩展的模型服务平台,这就是我们的机器学习模型服务平台Catwalk的用武之地。

Catwalk has evolved over time, adapting to the growing needs of our business and the ever-changing tech landscape. It has been a journey of continuous learning and improvement, with each step bringing new challenges and opportunities.

Catwalk随着时间的推移不断发展,适应了我们业务的不断增长和不断变化的技术环境。这是一段持续学习和改进的旅程,每一步都带来了新的挑战和机遇。

Evolution of the platform

平台的演进

Before Catwalk’s debut as our dedicated model serving platform, data scientists across the company employed various ad-hoc approaches to serve ML models. These included:

在Catwalk作为我们专用的模型服务平台之前,公司中的数据科学家采用了各种临时方法来提供ML模型的服务。其中包括:

  • Shipping models online using custom solutions.
  • 使用自定义解决方案在线发布模型。
  • Relying on backend engineering teams to deploy and manage trained ML models.
  • 依赖后端工程团队来部署和管理训练好的机器学习模型。
  • Embedding ML logic within Go backend services.
  • 将ML逻辑嵌入Go后端服务中。

These methods, however, led to several challenges, undercovering the need for a unified, company-wide platform for serving machine learning models:

然而,这些方法带来了一些挑战,揭示了需要一个统一的、全公司范围内的机器学习模型服务平台的需求:

  • Operational overhead: Data scientists oft...
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