Automating the Data Science Lifecycle: CI/CD for Machine Learning Deployment

Author(s)	Ms. Swathi Suddala
Country	United States
Abstract	The incorporation of Continuous Integration (CI) and Continuous Deployment (CD) into the machine learning (ML) lifecycle is essential for facilitating the effective transition of models from the development phase to production. Unlike conventional software, ML workflows face distinct challenges such as data versioning, model drift, hyperparameter optimization, and limitations in computational resources. This paper explores optimal practices for automating the data science lifecycle through CI/CD methodologies, focusing on critical elements like automated data validation, model retraining pipelines, and deployment orchestration. We analyze the significance of infrastructure-as-code, Docker containerization, model registries, and monitor frameworks in enhancing ML operations. Additionally, we propose a robust framework that ensures reproducibility, scalability, and reliability in the deployment of ML models. The study also highlights sophisticated CI/CD strategies tailored for machine learning, emphasizing the vital role of MLOps practices in maintaining model integrity within ever-evolving production settings.
Keywords	Continuous Integration, Continuous Deployment, Machine Learning Operations, Data Versioning, Infrastructure-as-Code, AutoML, Kubeflow, DevOps for ML, TensorFlow Extended, MLflow
Field	Computer > Data / Information
Published In	Volume 4, Issue 1, January-February 2022
Published On	2022-01-05

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International Journal For Multidisciplinary Research