May 2022 Summaries

A founding engineer at Arize shares their journey and insights on what it takes to be effective in this role, which requires wearing multiple hats such as technical individual contributor, customer support, product management, project management, recruiting, and content marketing. The author, Manisha Sharma, joined Arize after having experience in frontend engineering and senior frontend engineering at Pandora and Slack, respectively, and found that the startup environment aligned closely with her engineering goals. She identifies seven essential elements of effective founding engineers, including humility to admit what they don't know, summoning grace amid chaos, taking ownership, thriving under ambiguity, knowing their strengths and weaknesses, making tradeoffs wisely, and trusting their fellow founders.

"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."

Jiazhen Zhu leads the end-to-end data team at Walmart Global Governance DSI, focusing on building a better platform through data-driven decisions and data-powered products. He oversees both data engineering and machine learning, giving him a unique vantage point into the interrelated worlds of DataOps, MLOps, and data science. Zhu emphasizes the importance of trust in AI models, suggesting that simpler models like linear regression are often preferable due to their easier explanations. He also highlights the significance of model explainability and monitoring for successful MLOps.