Sarah Andrews
About Sarah Andrews
Sarah Andrews is a writer who focuses on the challenges and evolving methodologies in machine learning and data science.
Sarah Andrews Articles
Sarah Andrews has authored insightful articles focusing on machine learning and large language models (LLMs). Her notable works include 'Why Most LLM App POCs Fail' and 'Machine Learning Is About Statistics After All: A Series of Vignettes, Part 1'. These pieces discuss the nuanced challenges and considerations necessary for successful machine learning applications.
Reliability Challenges in LLM Applications
Sarah Andrews frequently writes about the reliability challenges in LLM-powered applications. Her articles delve into the complexities and potential pitfalls of these technologies, offering readers a deeper understanding of what it takes to ensure reliability and performance in LLM deployments.
Traditional Statistics in Data Science
Through her writing, Sarah Andrews explores the diminishing importance of traditional statistics in the field of data science. By examining how contemporary data science practices are evolving, she provides valuable insights into the shifting landscape and the implications for practitioners.
Strategies for Engaging with Machine Learning Reliability Challenges
Sarah Andrews discusses various strategies for engaging with reliability challenges in machine learning projects. Her writings provide practical guidance and frameworks that can help professionals address and mitigate potential issues, ultimately contributing to more robust and reliable machine learning systems.