Counterfactual Evaluation Policy for Machine Learning Models

How to monitor models whose actions prevent us from observing ground truth?The goal of monitoring any system is to track its health. In the context of machine learning, it is crucial to track the performance of the models we are serving in production. It can help us inform when our models are not fresh anymore and retraining of the model is required. It can also help us detect abuse in cases like fraud detection where there could be adversarial actors trying to harm the model.

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Labels — The Cornerstone of Modern Artificial Intelligence

Supervised Learning

Supervised Learning (SL) algorithms automatically detect patterns in any kind of data — but to do so, these algorithms must first see many labeled samples. In use cases such as intent classification, models like deep neural networks are provided with pairs of sentences (“Please set an alarm for 6 am”) and the desired outputs for given inputs (“set alarm”). Unlike human learners, SL algorithms need lots of examples. Therefore, for SL to excel in the way it is meant to be, a correct labeling strategy is critical to successful implementation.

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Beyond Digital Transformation: AI modernizing core technology across the industries to solve…

Digital transformation

Despite diversified industries, the majority of the businesses worldwide are leveraging the potential of artificial intelligence (AI) considering its benefits to solve long-standing problems. Emerging as a well-positioned technology to combat challenges, it delivers powerful, cost-saving and valuable insights to improve your organizational operations for seamless work processes.

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