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Machine learning in practice often involves complex pipelines for datacleansing, feature engineering, preprocessing, and prediction. These pipelinesare composed of operators, which have to be correctly connected and whosehyperparameters must be correctly configured. Unfortunately, it is quite commonfor certain combinations of datasets, operators, or hyperparameters to causefailures. Diagnosing and fixing those failures is tedious and error-prone andcan seriously derail a data scientist's workflow. This paper describes anapproach for automatically debugging an ML pipeline, explaining the failures,and producing a remediation. We implemented our approach, which builds on acombination of AutoML and SMT, in a tool called Maro. Maro works seamlesslywith the familiar data science ecosystem including Python, Jupyter notebooks,scikit-learn, and AutoML tools such as Hyperopt. We empirically evaluate ourtool and find that for most cases, a single remediation automatically fixeserrors, produces no additional faults, and does not significantly impactoptimal accuracy nor time to convergence.

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