Serg Masis

Today I had the pleasure of interviewing Serg Masis. Serg is a Data Scientist at Syngenta, a leading agribusiness company with a mission to improve global food security, and a bestselling author of "Interpretable Machine Learning with Python", and upcoming "DIY AI". Before that, he had a prior career in entrepreneurship, web and app development, and analytics for over fifteen years. He's passionate about improving decision-making with data, learning by building AI systems from scratch, and empowering others that wish to do the same. In this episode Serg teaches me all about how data science has changed how food is grown, we also touch on the recent advancements in AI and their implications on DIY AI. Serg's Links: LinkedIn: linkedin.com/in/smasisTwitter: https://twitter.com/smasisWebsite: https://www.serg.ai/GitHub: https://github.com/smasis001Mastodon: https://mastodon.social/@smasis@masto.aiSerg's Book: https://amzn.to/3GgpHHA

Serg Masis, a Climate & Agronomic Data Scientist at Syngenta and author of "Interpretable Machine Learning with Python," dives deep into the challenges of machine learning interpretability. He discusses the ethical ramifications of data bias, sharing insights into technical and non-technical solutions to address these issues. Serg highlights the real-world implications of misapplied AI, like in a home valuation case study. Plus, he sheds light on SHAP values and their role in understanding model predictions, advocating for fairness and transparency in AI.