Avik Basu
Avik Basu is a Staff Data Scientist passionate about building intelligent, scalable systems that blend research with practical impact. With extensive experience in time series modeling, anomaly detection, and explainable AI, he focuses on making machine learning robust, interpretable, and production-ready.
Avik is a frequent speaker at conferences like PyCascades, PyData and KubeCon, where he shares insights on topics such as reproducible ML workflows, ML-driven observability, etc. He is also an active contributor to the open-source ecosystem, serving as a maintainer of the real-time data processing framework Numaflow and a reviewer for scientific Python projects.
Outside of work, he explores the intersection of machine learning, personal finance, and open-source tools, aiming to build software that is accessible, self-hostable, and privacy-focused. He is driven by a strong belief in community, transparency, and empowering others through education and mentorship.
Session
As machine learning models become more accurate and complex, explainability remains essential. Explainability helps not just with trust and transparency but also with generating actionable insights and guiding decision-making. One way of interpreting the model outputs is using SHapley Additive exPlanations (SHAP). In this talk, I will go through the concept of Shapley values and its mathematical intuition and then walk through a few real-world examples for different ML models. Attendees will gain a practical understanding of SHAP's strengths and limitations and how to use it to explain model predictions in their projects effectively.