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Most of us take for granted that food is always available to us when we need it. Our local supermarkets have shelves stacked with produce from all corners of the world. Rarely do we stop to think that the items in our shopping carts have been on a long journey involving months of work by many people. How does all this food get produced in the first place, reliably, consistently and to a high standard? How do we combine and utilise scarce resources to feed billions of people around the world every day? I recently caught up with Serg Masis to answer these questions and understand how data science is used to optimise food production around the world. Serg is a Climate & Agronomic Data Scientist at global agriculture company Syngenta and author of the book ‘Interpretable Machine Learning with Python’. In this episode of Leaders of Analytics, we discuss: The biggest challenges facing our global food system and how data science can help solve theseHow data science is used to help the environmentWhy Serg wrote the book ‘Interpretable Machine Learning with Python’ and why we should read itHow to make models more interpretable, and much more.Connect with Serg: Serg's website: https://www.serg.ai/#about-me Serg on LinkedIn: https://www.linkedin.com/in/smasis/ Serg's books from Packt: https://www.packtpub.com/authors/serg-masis

One of the biggest challenges facing the adoption of machine learning and AI in Data Science is understanding, interpreting, and explaining models and their outcomes to produce higher certainty, accountability, and fairness.

Serg Masis is a Climate & Agronomic Data Scientist at Syngenta and the author of the book, Interpretable Machine Learning with Python. For the last two decades, Serg has been at the confluence of the internet, application development, and analytics. Serg is a true polymath. Before his current role, he co-founded a search engine startup incubated by Harvard Innovation Labs, was the proud owner of a Bubble Tea shop, and more.

Throughout the episode, Serg spoke 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.