Wesley Boelrijk

Forecasting time series can be messy, data is often missing, noisy, or full of structural changes like holidays, outliers, or evolving patterns. This talk shows how to build interpretable time series decomposition models using PyMC, a modern probabilistic programming library.

In the world of computer vision, the focus is often on cutting-edge neural network architectures. However, the true impact usually lies in designing a robust system around the model to solve real-world business challenges.

Building and fine-tuning models is exciting, but how do you know your model keeps performing in the way you carefully designed it? Bringing your model to production without adding any monitoring is like flying on autopilot, but blindfolded.