Hakim Elakhrass talks about post-deployment data science, the real-world use cases for tools like NannyML, the potentially catastrophic effects of unmonitored models in production, the most important skills for modern data scientists to cultivate, and more.
Serg Masis talks about the different challenges affecting model interpretability in machine learning, how bias can produce harmful outcomes in machine learning systems, the different types of technical and non-technical solutions to tackling bias, the future of machine learning interpretability, and much more.
Anjali Samani shares what it takes to become a mature data organization and how to build an impactful, diverse data team, the hallmarks of a mature data organization, how to measure ROI on data initiatives, how Salesforce implements its data science function, and how to utilize strong relationships to develop trust with internal stakeholders and the data team.
Sandra Kublik and Shubham Saboo, authors of GPT-3: Building Innovative NLP Products Using Large Language Models shares insights about what makes GPT-3 unique, the transformative use-cases it has ushered in, the technology powering GPT-3, its risks and limitations, whether scaling models is the path to “Artificial General Intelligence”, and more.
Elettra Damaggio shares how data leaders can balance short-term wins with long-term goals, how to earn trust with stakeholders, major challenges when launching a data science function, and advice she has for new and aspiring data practitioners.
Brian Richardi talks about his experience as a data science leader transitioning from Finance. He provides insights into utilizing collaboration and effective communication to drive value while leading the data science finance function at Stryker.
In this episode, we talk about how Glenn Hofmann built New York Life Insurance’s 50-person data science and AI function, how they utilize skillsets to offer different career paths for data scientists, and much more.
Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson, discusses how the healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient information and accounting for the real-world impact of AI and machine learning on patient care and experience.