Modern implantable cardiologic devices communicate via radio frequency techniques and nearby gateways to a backend server on the internet. Those implanted devices, gateways, and servers form an ecosystem of proprietary hardware and protocols that process sensitive medical data and is often vital for patients’ health.

Machine learning (ML) and data science workflows are inherently exploratory. Data scientists pose hypotheses, integrate the necessary data, and run ML pipelines of data cleaning, feature engineering, model selection and hyper-parameter tuning. The repetitive nature of these workflows, and their hierarchical composition from building blocks exhibits high computational redundancy. Existing work addresses this redundancy with coarse-grained lineage tracing and reuse for ML pipelines. This approach allows using existing ML systems, but views entire algorithms as black boxes, and thus, fails to eliminate fine-grained redundancy and to handle internal non-determinism. In this paper, we introduce LIMA, a practical framework for efficient, fine-grained lineage tracing and reuse inside ML systems. Lineage tracing of individual operations creates new challenges and opportunities. We address the large size of lineage traces with multi-level lineage tracing and reuse, as well as lineage deduplication for loops and functions; exploit full and partial reuse opportunities across the program hierarchy; and integrate this framework with task parallelism and operator fusion. The resulting framework performs fine-grained lineage tracing with low overhead, provides versioning and reproducibility, and is able to eliminate fine-grained redundancy. Our experiments on a variety of ML pipelines show performance improvements up to 12.4x.