Designing Production-Friendly Machine Learning
Summary: Production ML challenges—cost and failure modes—analyzed by DAWN Lab and Databricks. Proposes two directions: standardized ML platforms (MLflow) to ease deployment, and production-friendly ColBERT with updateable corpora for low compute, interpretability, and rapid updates as an LLM alternative. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Matei Zaharia (Databricks; Stanford University)
BibTeX Citation
@article{zaharia_vldb21,
title = {{Designing Production-Friendly Machine Learning}},
author = {Zaharia, Matei},
journal = {PVLDB},
series = {{VLDB} '21},
volume = {14},
number = {13},
pages = {3420--3420},
doi = {10.14778/3484224.3484241},
url = {https://doi.org/10.14778/3484224.3484241},
year = {2021}
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