  Machine learning training data is often dynamic in real-world use cases,
i.e., data is added or removed and may experience distribution shifts over
time. Models must incorporate this evolving training data to improve
generalization, adapt to potential distribution shifts, and adhere to privacy
regulations. However, the cost of model (re)training is proportional to how
often the model trains and on how much data it trains on. While ML research
explores these topics in isolation, there is no end-to-end open-source platform
to facilitate the exploration of model retraining and data selection policies
and the deployment these algorithms efficiently at scale.
  We present Modyn, a platform for model training on dynamic datasets that
enables sample-level data selection and triggering policies. Modyn orchestrates
continuous training pipelines while optimizing the underlying system
infrastructure to support fast access to arbitrary data samples for efficient
data selection. Modyn's extensible architecture allows users to run training
pipelines without modifying the platform code, and enables researchers to
effortlessly extend the system. We evaluate Modyn's training throughput,
showing that even in memory-bound recommendation systems workloads, Modyn is
able to reach 80 to 100 % of the throughput compared to loading big chunks of
data locally without sample-level data selection. Additionally, we showcase
Modyn's functionality with three different data selection policies.
