Through extensive experience develo** and explaining machine learning (ML)
applications for real-world domains, we have learned that ML models are only as …

Few-shot named entity recognition (NER) systems recognize entities using a few labeled
training examples. The general pipeline consists of a span detector to identify entity spans in …

Recent studies show that an overwhelming majority of emails are machine-generated and
sent by businesses to consumers. Many large email services are interested in extracting …

Machine Learning Health Operations (MLHOps) is the combination of processes for reliable,
efficient, usable, and ethical deployment and maintenance of machine learning models in …

Email has been an essential communication medium for many years. As a result, the
information accumulated in our mailboxes has become valuable for all of our personal and …

This paper presents a case study of migrating a privacy-safe information extraction system in
production for Gmail from a traditional rule-based architecture to a machine-learned …

It is an essential product requirement of Yahoo Mail to distinguish between personal and
machine-generated emails. The old production classifier in Yahoo Mail was based on a …

In emails, information abounds. Whether it be a bill reminder, a hotel confirmation, or a
ship** notification, our emails contain useful bits of information that enable a number of …

Machine Learning Health Operations (MLHOps) is the combination of processes for reliable,
efficient, usable, and ethical deployment and maintenance of machine learning models in …

Machine generated business-to-consumer (B2C) emails such as receipts, newsletters, and
promotions constitute a large portion of users' inboxes today. These emails reflect the users' …