On Continuous Integration / Continuous Delivery for Automated Deployment
of Machine Learning Models using MLOps
- URL: http://arxiv.org/abs/2202.03541v1
- Date: Mon, 7 Feb 2022 22:04:38 GMT
- Title: On Continuous Integration / Continuous Delivery for Automated Deployment
of Machine Learning Models using MLOps
- Authors: Satvik Garg, Pradyumn Pundir, Geetanjali Rathee, P.K. Gupta, Somya
Garg, Saransh Ahlawat
- Abstract summary: This research provides a more in-depth look at the machine learning lifecycle and the key distinctions between DevOps and MLOps.
In the MLOps approach, we discuss tools and approaches for executing the CI/CD pipeline of machine learning frameworks.
Following that, we take a deep look into push and pull-based deployments in Github Operations (GitOps)
- Score: 1.2885809002769633
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Model deployment in machine learning has emerged as an intriguing field of
research in recent years. It is comparable to the procedure defined for
conventional software development. Continuous Integration and Continuous
Delivery (CI/CD) have been shown to smooth down software advancement and speed
up businesses when used in conjunction with development and operations
(DevOps). Using CI/CD pipelines in an application that includes Machine
Learning Operations (MLOps) components, on the other hand, has difficult
difficulties, and pioneers in the area solve them by using unique tools, which
is typically provided by cloud providers. This research provides a more
in-depth look at the machine learning lifecycle and the key distinctions
between DevOps and MLOps. In the MLOps approach, we discuss tools and
approaches for executing the CI/CD pipeline of machine learning frameworks.
Following that, we take a deep look into push and pull-based deployments in
Github Operations (GitOps). Open exploration issues are also identified and
added, which may guide future study.
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