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MLflow is a popular open source platform for managing ML development, including experiment tracking, reproducibility, and deployment. In this paper, we discuss user feedback collected since MLflow was launched in 2018, as well as three major features we have introduced in response to this feedback: a Model Registry for collaborative model management and review, tools for simplifying ML code instrumentation, and experiment analytics functions for extracting insights from millions of ML experiments.
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Stored only when both papers are in the corpus — 111 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.