Monitoring the Performance of Machine Learning Models in Production
|© 2022 by IJCTT Journal|
|Year of Publication : 2022|
|Authors : Satyanarayan Raju Vadapalli|
|DOI : 10.14445/22312803/IJCTT-V70I9P105|
How to Cite?
Satyanarayan Raju Vadapalli, "Monitoring the Performance of Machine Learning Models in Production," International Journal of Computer Trends and Technology, vol. 70, no. 9, pp. 38-42, 2022. Crossref, https://doi.org/10.14445/22312803/IJCTT-V70I9P105
Machine learning (ML) models have become vital decision-making components for many businesses in the last decade. However, the performance of ML models degrades over time due to multiple economic or environmental factors that can lead to non-optimal decision-making. With organizations having tens or even hundreds of ML models deployed in production, it is important to ensure the models perform the way they were trained to perform. Additionally, models need to be retrained every few weeks or months to adapt to the evolving environment that affects the model performance. In this article, we discuss an approach that can be used to proactively identify issues with model output and inform the developers and data scientists when it’s time to retrain the model. Given the importance of input data quality in model performance, our approach`s significant attention is coming up with ways to identify data quality issues and take proactive measures to mitigate the associated risks. This monitoring approach is currently deployed on multiple production models, generating automated alerts on models or data drifts, enabling the data scientists to take corrective actions.
Drift detection, Machine learning, MLOPs, Monitoring, Observability.
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