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Towards Observability for Machine Learning Pipelines with Shreya Shankar

Posted Jul 14
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SPEAKER
Shreya Shankar
Shreya Shankar
Shreya Shankar
PhD Student at the RISELab @ UC Berkeley

Shreya Shankar is doing her PhD in data management for machine learning (ML) systems at UC Berkeley. She works on streaming systems and open-source tooling for ML monitoring. Previously, she was the first ML engineer at a startup, did deep learning research at Google Brain, and completed her BS and MS in computer science at Stanford.

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Shreya Shankar is doing her PhD in data management for machine learning (ML) systems at UC Berkeley. She works on streaming systems and open-source tooling for ML monitoring. Previously, she was the first ML engineer at a startup, did deep learning research at Google Brain, and completed her BS and MS in computer science at Stanford.

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SUMMARY

Software organizations are increasingly incorporating machine learning (ML) into their product offerings, driving a need for new data management tools. Many of these tools facilitate the initial development of ML applications, but sustaining these applications post-deployment is difficult due to lack of real-time feedback (i.e., labels) for predictions and silent failures that could occur at any stage, or component, of the ML pipeline (e.g., data distribution shift). We propose a new type of data management system that offers end-to-end observability, or visibility into complex system behavior, for ML pipelines through assisted (1) detection, (2) diagnosis, and (3) reaction to ML-related bugs. We describe new research challenges and suggest preliminary solution ideas in all three aspects. Finally, we introduce an example architecture for a “bolt-on” ML observability system, or one that wraps around existing tools in the stack.

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