Machine Learning

Maintaining Machine Learning Model Accuracy Through Monitoring

Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity. The post Maintaining Machine Learning Model Accuracy Through Monitoring appeared first on DoorDash Engineering Blog.

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How Pinterest Fights Spam Using Machine Learning

One tactic malicious actors enact is misusing a Pin’s image and linking to a malicious external website. Our models detect spam vectors, like Pin links, as well as users engaging in spammy behaviors. We quickly limit distribution of Pins with spam links and take direct action against users identified with a high confidence to be engaging in spammy behavior.

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Optimizing payments with machine learning

At Dropbox, we found that applying machine learning to our handling of customer payments has made us better at keeping subscribers happily humming along.

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Medical AI Needs Federated Learning, So Will Every Industry

A multi-hospital initiative sparked by the COVID-19 crisis has shown that, by working together, institutions in any industry can develop predictive AI models that set a new standard for both accuracy and generalizability. Published today in Nature Medicine, a leading peer-reviewed healthcare journal, the collaboration demonstrates how privacy-preserving federated learning techniques can enable the creation The post Medical AI Needs Federated Learning, So Will Every Industry appeared first on The Official NVIDIA Blog.

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Algorithm-Assisted Inventory Curation

Personalizing fashion at scale requires that we build an inventory whose size and complexity are as great as that of our client base. To support our inventory expansion and our broader supply chain management, Stitch Fix has developed a suite of algorithms to act as a new inventory recommender system.

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A Heuristic for Multiple Times Speed-up of Model Training

The first question that comes to mind is HOW? The answer is simple, reduce the number of datapoints. However, a more interesting question is the way in which datapoints are reduced.

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Introducing LinNét: Using Rich Image and Text data to Categorize Products at Scale

In this post, we’ll discuss how we evolved and modernized our product categorization model that increased our leaf precision by 8% while doubling our coverage. We’ll dive into the challenges of solving this problem at scale and the technical trade-offs we made along the way.

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