Debugging Machine Learning Pipelines

by   Raoni Lourenco, et al.
NYU college

Machine learning tasks entail the use of complex computational pipelines to reach quantitative and qualitative conclusions. If some of the activities in a pipeline produce erroneous or uninformative outputs, the pipeline may fail or produce incorrect results. Inferring the root cause of failures and unexpected behavior is challenging, usually requiring much human thought, and is both time-consuming and error-prone. We propose a new approach that makes use of iteration and provenance to automatically infer the root causes and derive succinct explanations of failures. Through a detailed experimental evaluation, we assess the cost, precision, and recall of our approach compared to the state of the art. Our source code and experimental data will be available for reproducibility and enhancement.


BugDoc: Algorithms to Debug Computational Processes

Data analysis for scientific experiments and enterprises, large-scale si...

Automatically Debugging AutoML Pipelines using Maro: ML Automated Remediation Oracle (Extended Version)

Machine learning in practice often involves complex pipelines for data c...

DNN Explanation for Safety Analysis: an Empirical Evaluation of Clustering-based Approaches

The adoption of deep neural networks (DNNs) in safety-critical contexts ...

Debugging Machine Learning Tasks

Unlike traditional programs (such as operating systems or word processor...

Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps

Root Cause Analysis (RCA) of any service-disrupting incident is one of t...

Identifying the root cause of cable network problems with machine learning

Good quality network connectivity is ever more important. For hybrid fib...

Please sign up or login with your details

Forgot password? Click here to reset