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Building LLM applications responsibly, safely and ready for production
How will large language models (LLMs) change machine learning workflows? And how can data scientists make sure to deploy LLMs responsibly?
Generative AI applications have been extensively implemented everywhere across many industries, but there is a lot more that developers must do to build LLMs models responsibly, and it’s difficult for developers to do this work from scratch and create safe and trustworthy applications.
In this session, we will explore emerging guidance to build production-ready LLMs and mitigate risks presented by LLMs, and how data scientists can use tools to implement best practices or any pre-built and customizable models from the open-source ecosystem.
Seminar Recording
Francesca Lazzeri, Ph.D. has over 15 years of experience in academic research, applied machine learning, AI innovation and engineering team management. She is author of a few books on applied machine learning and AI, such as:
- Machine Learning Governance for Managers (forthcoming, Springer Nature)
- Impact of Artificial Intelligence in Business and Society (2023, Routledge)
- Machine Learning for Time Series Forecasting with Python (2020, Wiley)
Francesca is Senior Director of Data Science and AI at Microsoft, where she leads an organization of talented data scientists and machine learning scientists building intelligent applications on the Cloud, utilizing data and techniques spanning from generative AI, time series forecasting, experimentation, causal inference, computer vision, natural language processing, reinforcement learning. Francesca is also Adjunct Professor of Python for machine learning and AI at Columbia University in New York City.
Before joining Microsoft, she was a Research Fellow at Harvard University in the Technology and Operations Management Unit.
Francesca is also Advisory Board Member of the AI-CUBE (Artificial Intelligence and Big Data CSA for Process Industry Users, Business Development and Exploitation) project, the Women in Data Science (WiDS) initiative, machine learning mentor at the Massachusetts Institute of Technology, and active member of the AI community.