The Comet Newsletter, Issue #4: A/B testing at Etsy, a new adversarial attach, the MLOps landscape, and more

The Comet Newsletter explores various topics in its fourth issue, including an examination of the DeepSloth adversarial attack on Adaptive Deep Neural Networks and Etsy's implementation of the CUPED method to enhance A/B testing with control variates. DeepSloth, introduced by researchers at the University of Maryland, targets Shallow-Deep Networks to slow down deep learning models by inducing network "overthinking," which increases cloud query latency, highlighting the need for practical solutions in machine learning security. Additionally, Etsy's Online Experimentation Science team discusses CUPED, which uses pre-experiment data to improve the accuracy and efficiency of online experiments by reducing variance without increasing sample size. The newsletter also delves into few-shot learning using GPT-Neo and Hugging Face's API to overcome data limitations and previews a three-part series on navigating the MLOps tooling landscape, which emphasizes the importance of tailored tool selection for machine learning projects.

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