Automating the Data Science Lifecycle: CI/CD for Machine Learning Deployment
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| Format: | Recurso digital |
| Sprache: | Englisch |
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Zenodo
2022
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| _version_ | 1866902265579700224 |
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| author | Suddala, Swathi |
| author_facet | Suddala, Swathi |
| contents | <p>The incorporation of Continuous Integration (CI) and Continuous Deployment (CD) into the machine <br>learning (ML) lifecycle is essential for facilitating the effective transition of models from the <br>development phase to production. Unlike conventional software, ML workflows face distinct challenges <br>such as data versioning, model drift, hyperparameter optimization, and limitations in computational <br>resources. This paper explores optimal practices for automating the data science lifecycle through CI/CD <br>methodologies, focusing on critical elements like automated data validation, model retraining pipelines, <br>and deployment orchestration. We analyze the significance of infrastructure-as-code, Docker <br>containerization, model registries, and monitor frameworks in enhancing ML operations. Additionally, <br>we propose a robust framework that ensures reproducibility, scalability, and reliability in the deployment <br>of ML models. The study also highlights sophisticated CI/CD strategies tailored for machine learning, <br>emphasizing the vital role of MLOps practices in maintaining model integrity within ever-evolving <br>production settings.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15328138 |
| institution | Zenodo |
| language | eng |
| publishDate | 2022 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Automating the Data Science Lifecycle: CI/CD for Machine Learning Deployment Suddala, Swathi Data Science Data analysis Machine Learning data versioning <p>The incorporation of Continuous Integration (CI) and Continuous Deployment (CD) into the machine <br>learning (ML) lifecycle is essential for facilitating the effective transition of models from the <br>development phase to production. Unlike conventional software, ML workflows face distinct challenges <br>such as data versioning, model drift, hyperparameter optimization, and limitations in computational <br>resources. This paper explores optimal practices for automating the data science lifecycle through CI/CD <br>methodologies, focusing on critical elements like automated data validation, model retraining pipelines, <br>and deployment orchestration. We analyze the significance of infrastructure-as-code, Docker <br>containerization, model registries, and monitor frameworks in enhancing ML operations. Additionally, <br>we propose a robust framework that ensures reproducibility, scalability, and reliability in the deployment <br>of ML models. The study also highlights sophisticated CI/CD strategies tailored for machine learning, <br>emphasizing the vital role of MLOps practices in maintaining model integrity within ever-evolving <br>production settings.</p> |
| title | Automating the Data Science Lifecycle: CI/CD for Machine Learning Deployment |
| topic | Data Science Data analysis Machine Learning data versioning |
| url | https://doi.org/10.5281/zenodo.15328138 |