Probabilistic models have proven to be a great tool for solving business-critical problems in fields such as marketing, demand forecasting, and risk-based optimization. One of the biggest challenges is scaling these models to large data sets and efficiently utilizing modern computing power.

This talk addresses the challenges of scaling probabilistic models using variational inference and other similar methods. We will explain the core concepts of variational inference in an accessible way, avoiding heavy mathematics. We will use practical examples with NumPyro and PyMC to demonstrate how to apply variational inference effectively. Starting with simple models and then showing applications with custom forecasting models and neural network components. Additionally, we will cover diagnostics such as simulation-based calibration and coverage to ensure model reliability. Our discussion will also include strategies for scaling, including mini-batch optimization and distributed computing.