  Machine learning (ML) applications become increasingly common in many
domains. ML systems to execute these workloads include numerical computing
frameworks and libraries, ML algorithm libraries, and specialized systems for
deep neural networks and distributed ML. These systems focus primarily on
efficient model training and scoring. However, the data science process is
exploratory, and deals with underspecified objectives and a wide variety of
heterogeneous data sources. Therefore, additional tools are employed for data
engineering and debugging, which requires boundary crossing, unnecessary manual
effort, and lacks optimization across the lifecycle. In this paper, we
introduce SystemDS, an open source ML system for the end-to-end data science
lifecycle from data integration, cleaning, and preparation, over local,
distributed, and federated ML model training, to debugging and serving. To this
end, we aim to provide a stack of declarative language abstractions for the
different lifecycle tasks, and users with different expertise. We describe the
overall system architecture, explain major design decisions (motivated by
lessons learned from Apache SystemML), and discuss key features and research
directions. Finally, we provide preliminary results that show the potential of
end-to-end lifecycle optimization.
