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Optimizing Machine Learning Workloads in Collaborative Environments

Summary: Introduces Experiment Graph (EG) to persist artifacts (data/models) as vertices and ML operations as edges for collaborative ML workloads. Proposes two materialization strategies and a linear-time reuse algorithm to cache artifacts and plan execution, yielding up to 10x speedups on repeats and ~50% on edits. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5930
Venue
SIGMOD
Year
2020
Pagerank
5.2326838e-05
Overall Rank
6,053 | 57.90%
DOI
10.1145/3318464.3389715

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