If you have searched for an item to buy on amazon.com, you have used Amazon Search services. At Amazon Search, we’re responsible for the search and discovery experience for our customers worldwide. In the background, we index our worldwide catalog of products, deploy highly scalable AWS fleets, and use advanced machine learning (ML) to match relevant and interesting products to every customer’s query.
Our scientists regularly train thousands of ML models to improve the quality of search results. Supporting large-scale experimentation presents its own challenges, especially when it comes to improving the productivity of the scientists training these ML models.
In this post, we share how we built a management system around Amazon SageMaker training jobs, allowing our scientists to fire-and-forget thousands of experiments and be notified when needed. They can now focus on high-value tasks and resolving algorithmic errors, saving 60% of their time.
The challenge
At Amazon Search,

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