Machine Learning in an Agile World: Keeping Models Fresh with Continuous Intelligence
Increasingly, machine learning is finding its way into production systems. More and more people are learning data science, and AI tools and frameworks are getting better every day. Nevertheless, researching, implementing, deploying, and maintaining a machine learning application is still a challenge, and many struggle to develop AI systems with the same pace, flexibility, and agility that they develop deterministic software and service. In this talk, we'll explore Continuous Intelligence—a set of working principles to keep machine learning models up-to-date and maintainable, including why it is critically important that they are so.
Emily has over ten years of experience in scientific computing and engineering research and development. She has a background in mathematical analysis, with a focus on probability theory and numerical analysis. She is currently working in Python, though she has a background that includes C#/.Net, Unity3D, SQL, and MATLAB. In addition, she has experience in statistics and experimental design, and has served as Principal Investigator in clinical research projects. An advocate for social justice, Emily is an activist and survivor of the 2017 Charlottesville neo-Nazi attacks. She was named as one of 2018’s most influential feminists by Bitch Magazine for her work in shining a light on far-right violence with her project, First Vigil.
Autres talks de Emily
2019 - Beyond Ethics — Technological Citizenship in Changing TimesEN
Over the past few years, the relationship between technology, technologists, and the state has come under scrutiny. With many people becoming angry with the unchecked influence that technology has on society and government, people are starting to question the ethical duties of technologists. These discussions are too late. This talk will challenge the audience, and with a radical lens, will explore how something more than simple ethics is needed to address the damage that has already happened—and to mitigate the damage to come.