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• AUTHOR: codevision AI engine
• DZone IoT

A developer faced an issue where their ML model misclassified groceries as entertainment expenses. By implementing distributed tracing using OpenTelemetry and Jaeger, they were able to quickly identify a caching bug that caused the misclassification. This article explains how tracing infrastructure can transform debugging from a frustrating task into a manageable process. Ramya Boorugula, the author, shares a real-world example of how tracing helped resolve a complex issue in a distributed ML system. It is worth reading because it provides a practical guide for developers working on distributed projects. Readers will learn how to set up tracing tools to debug their own distributed ML systems effectively.

Key facts

• The author’s ML model started misclassifying groceries as entertainment expenses.
• Distributed tracing with OpenTelemetry and Jaeger helped identify a caching bug causing the issue.
• The author had decomposed their monolith finance tracker into multiple microservices, making debugging challenging.
• Implementing tracing infrastructure transformed the debugging process from frustrating to manageable.
• The caching bug was due to a string formatting error that truncated the user ID in the Redis cache key.