"Elizabeth Hutton, a lead machine learning engineer at Cisco's Webex Contact Center AI team, leads building in-house AI solutions from research to production. She has three patents pending and works on natural language processing (NLP) tasks such as question-answering and summarization. Her work relies on providing good customer experiences across billions of monthly calls. Hutton transitioned from academia to industry through Insight Data Science, a role that helped her prepare for interviews and understand the industry. She advises aspiring ML engineers to develop research experience, but notes that an advanced degree can be helpful for research roles. In her day-to-day work, Hutton is responsible for data gatekeeping, model development, and software production. Her team uses tools like Snorkel to label data and Weights & Biases for experimentation. Hutton prioritizes understanding end-requirements, such as latency and scale, when developing models for production. She emphasizes the importance of testing and evaluating models in the lab before deployment, using custom metrics and feedback collection to ensure model performance. Her team uses Checklist for unit testing language models and has a cloud-first platform that serves both cloud clients and on-premise users."