# Building Observable ML Pipelines: Logs, Metrics, and Tracing

Page: https://stenobird.com/podcast/the-business-compass-llc-podcasts-7078188/building-observable-ml-pipelines-logs-metrics-and-tracing
Text version: https://stenobird.com/podcast/the-business-compass-llc-podcasts-7078188/building-observable-ml-pipelines-logs-metrics-and-tracing.md
Podcast: [The Business Compass LLC Podcasts](https://stenobird.com/podcast/the-business-compass-llc-podcasts-7078188)
Published: 2026-04-27T04:39:12+00:00
Episode link: https://podcast.businesscompassllc.com/e/building-observable-ml-pipelines-logs-metrics-and-tracing/
Audio file: https://mcdn.podbean.com/mf/web/n7d3thzhhgagzd6r/6e326e85-03fb-4809-b36d-e27efd6fedae.mp3
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JSON: https://stenobird.com/v1/public/podcasts/the-business-compass-llc-podcasts-7078188/episodes/building-observable-ml-pipelines-logs-metrics-and-tracing
Duration seconds: 806

## Resource

Building observable ML pipelines transforms chaotic machine learning workflows into transparent, debuggable systems that teams can actually trust in production. Without proper ML pipeline observability, data scientists and MLOps engineers spend countless hours playing detective when models fail, data drift occurs, or performance suddenly tanks.

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## Transcript

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