PyData Berlin 2023 August Meetup

Welcome to the next Pydata Berlin meetup !!

We would like to welcome you all starting at 18:00. The talks begin around 18:30.

Please provide your first and last name for the registration because this is required for the venue's entry policy. If you cannot attend, please cancel your spot so others are able to join as the space is limited.

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The Lineup for the Evening

Talk 1: Cohort Revenue & Retention Analysis: A Bayesian Approach

Speaker: Juan Camilo Orduzs a Mathematician (Ph.D. Humboldt Universität zu Berlin) and data scientist at Wolt. He is interested in interdisciplinary applications of mathematical methods. In particular, time series analysis, bayesian methods, and causal inference.

Abstract: We present a Bayesian approach to model cohort-level retention rates and revenue over time. We use Bayesian additive regression trees (BART) to model the retention component which we couple with a linear model to model the revenue component. This method is flexible enough to allow adding additional covariates to both model components. This Bayesian model allows us to quantify the uncertainty in the estimation, understand the effect of the covariates on the retention through partial dependence plots (PDP), individual conditional expectation (ICE) plots and last but not least forecast the future revenue, and retention rates.

Talk 2: The Mythical Machine Learning Pipeline

Speaker: Jim Dowling is CEO of Hopsworks and an Associate Professor at KTH Royal Institute of Technology. He is lead architect of the open-source Hopsworks Feature Store platform. He organizes the annual feature store summit and co-organizes PyData Stockholm.

Abstract: Developers often talk about ML pipelines as a concrete building block for production ML systems. However, these monolithic creatures are almost never seen in production. Instead, real-world ML systems are typically factored into manageable pipelines with dedicated tasks. In this talk, we show how to build ML systems as a composition of feature pipelines, training pipelines, and inference pipelines with a shared data layer.

There will be slots for 2-3 Lightning Talks (5-6 Minutes for each).
Kindly let us know if you would like to present something at the start of the meetup :)

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