Automating Model Retraining in DevOps Pipelines with MLOps
Addressing Model Drift and Data Evolution
Keywords:
model retraining, DevOps, MLOps, model drift, data evolutionAbstract
As machine learning (ML) systems become increasingly integrated into business processes, the challenges associated with maintaining model performance over time have gained prominence. One significant challenge is model drift, which refers to the degradation of model accuracy due to changes in data distributions. To ensure ongoing model relevance in dynamic environments, automating model retraining within DevOps pipelines has emerged as a critical area of focus. This paper explores the strategies and techniques for automating model retraining using MLOps practices. It discusses the importance of continuous monitoring, data versioning, and pipeline orchestration in addressing model drift and data evolution. By implementing these MLOps strategies, organizations can streamline the retraining process, reduce downtime, and enhance the overall effectiveness of their machine learning initiatives. This paper concludes with best practices and future directions for research in automating model retraining.
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