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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Online-Zugang: | https://arxiv.org/abs/2408.11112 |
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| _version_ | 1866910760688418816 |
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| author | Nogare, Diego Silveira, Ismar Frango |
| author_facet | Nogare, Diego Silveira, Ismar Frango |
| contents | In recent years, Data Science has become increasingly relevant as a support tool for industry, significantly enhancing decision-making in a way never seen before. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. Research results shows MLOps is a constantly evolving discipline, with challenges and solutions for integrating development and production environments, publishing models in production environments, and monitoring models throughout the end to end development lifecycle. This paper contributes to the understanding of MLOps techniques and their most diverse applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11112 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps Nogare, Diego Silveira, Ismar Frango Machine Learning In recent years, Data Science has become increasingly relevant as a support tool for industry, significantly enhancing decision-making in a way never seen before. In this context, the MLOps discipline emerges as a solution to automate the life cycle of Machine Learning models, ranging from experimentation to monitoring in productive environments. Research results shows MLOps is a constantly evolving discipline, with challenges and solutions for integrating development and production environments, publishing models in production environments, and monitoring models throughout the end to end development lifecycle. This paper contributes to the understanding of MLOps techniques and their most diverse applications. |
| title | Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.11112 |