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The Modern ML Monitoring Mess: Categorizing Post-Deployment Issues (2/4)

sh-reya.com · published 13 December 2021

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The Modern ML Monitoring Mess: Rethinking Streaming Evaluation (1/4) Read at the source ↗

https://www.geekwire.com/2021/ibuying-algorithms-failed-zillow-says-business-worlds-love-affair-ai/

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https://www.teamblind.com/post/Did-Zillow-fail-miserably-due-to-forcing-application-of-Machine-Learning-o8fCCZCD

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https://iiot-world.com/industrial-iot/connected-industry/why-85-of-machine-learning-projects-fail/

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https://fortune.com/2020/02/04/artificial-intelligence-data-labeling-labelbox/

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https://fortune.com/2021/08/09/snorkel-ai-funding-data-labeling-startup/

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https://venturebeat.com/2021/12/07/data-labeling-will-fuel-the-ai-revolution/

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https://searchenterpriseai.techtarget.com/news/252509086/Women-led-data-labeling-AI-startup-raises-70M-in-Series-B

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For companies that use ML, labeled data is the key differentiator Read at the source ↗

https://neptune.ai/blog/how-to-monitor-your-models-in-production-guide

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The Modern ML Monitoring Mess: Failure Modes in Extending Prometheus (3/4) Read at the source ↗

https://prometheus.io/

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https://twitter.com/xLaszlo

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