Shreya Shankar • 1/31/2022

The Modern ML Monitoring Mess: Research Challenges (4/4)

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This article concludes a series on modern ML monitoring by outlining a research agenda focused on data management challenges post-deployment. It uses a taxi tip prediction example to discuss coarse-grained vs. fine-grained metric computation, the difficulty of real-time Service Level Indicator (SLI) tracking, and issues like changing data subpopulations. The content is technical, addressing ML evaluation, monitoring architectures, and engineering problems in production systems.

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#Machine Learning #Data Management #Model Evaluation
The Modern ML Monitoring Mess: Research Challenges (4/4)

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