Climate and Agronomic Data Scientist at Syngenta
Serg Masís has been at the confluence of the internet, application development, and analytics for the last two decades. Currently, he’s a Data Scientist at Syngenta, a leading agribusiness company improving global food security and writing the 2nd edition of his bestselling book titled “Interpretable Machine Learning with Python”. Before that, he co-founded a search engine startup, incubated by Harvard Innovation Labs, to expose users to new places and events. Whether it pertains to leisure activities, plant diseases, or customer lifetime value, Serg is passionate about providing the link between data and decision-making — and interpretation helps bridge this gap more robustly.
WATCH LIVE: april 20 @ 1:30PM – 2:00PM ET
We assume that data holds all the answers to how to automate decisions. To this end, we build data pipelines and train and deploy machine learning models that turn inputs into outputs. But it isn’t that simple. Data holds plenty of answers, but the process needs more guidance to yield models that we can trust to replace/enhance human decision-making. To this end, XAI or Interpretable ML has the right toolset. Trust is mission-critical for any technology, so if AI solutions are to supplant software and humans, AI must reach the reliability standards currently expected from software and humans. For that to happen, XAI will be more widely adopted, but also the roles of data scientist and ML engineer will evolve. We will examine examples of XAI methods and discuss how they can revolutionize the way we train, evaluate and deploy machine learning models.