In real-world machine learning (ML) pipelines, datasets are continuously growing. Models must incorporate this new training data to improve generalization and adapt to potential distribution shifts. The cost of model retraining is proportional to how frequently the model is retrained and how much data it is trained on, which makes the naive approach of retraining from scratch each time impractical. We present Modyn, a data-centric end-to-end machine learning platform. Modyn's ML pipeline abstraction enables users to declaratively describe policies for continuously training a model on a growing dataset. Modyn pipelines allow users to apply data selection policies (to reduce the number of data points) and triggering policies (to reduce the number of trainings). Modyn executes and orchestrates these continuous ML training pipelines. The system is open-source and comes with an ecosystem of benchmark datasets, models, and tooling. We formally discuss how to measure the performance of ML pipelines by introducing the concept of composite models, enabling fair comparison of pipelines with different data selection and triggering policies. We empirically analyze how various data selection and triggering policies impact model accuracy, and also show that Modyn enables high throughput training with sample-level data selection.

Sustainability in computing is critical as environmental concerns rise. The cloud industry's carbon footprint is significant and rapidly growing. We show that dynamic geospatial shifting of cloud workloads to regions with lower carbon emission energy sources, particularly for more portable cloud workloads such as serverless applications, has a high potential to lower operational carbon emissions. To make the case, we build a comprehensive framework called Caribou that offloads serverless workflows across geo-distributed regions. Caribou requires no change in the application logic, nor on the provider side. It dynamically determines the best deployment plans, automatically (re-) deploys functions to appropriate regions, and redirects traffic to new endpoints. In reducing operational carbon through fine-grained, function-level offloading, Caribou does not undermine standard metrics such as performance and cost. We show how this approach can reduce the carbon footprint by an average of 22.9% to 66.6% across the North American continent. We demonstrate how a detailed specification of location constraints (e.g., to ensure compliance of one stage) can allow emission reductions for workflows (e.g., by offloading other stages). By showcasing the feasibility of carbon-aware geospatial application deployment, Caribou aims to push the boundaries of system techniques available to curtail cloud carbon emissions and provide a framework for future research.