DataFramed

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, Director of Data Science & Data Intelligence at Salesforce, joins the show to discuss what it takes to become a mature data organization and how to build an impactful, diverse data team. As a data leader with over 15 years of experience, Anjali is an expert at assessing and deriving maximum value out of data, implementing long-term and short-term strategies that directly enable positive business outcomes, and how you can do the same.

You will learn the hallmarks of a mature data organization, how to measure ROI on data initiatives, how Salesforce implements its data science function, and how you can utilize strong relationships to develop trust with internal stakeholders and your 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.