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- Type of Document: M.Sc. Thesis
- Language: Farsi
- Document No: 57271 (05)
- University: Sharif University of Technology
- Department: Electrical Engineering
- Advisor(s): Hashemi, Matin
- Abstract:
- In the current era, artificial intelligence and machine learning have become vital and widely used technologies across various industries. These technologies enable companies and organizations to optimize processes, predict trends, and uncover hidden patterns in data with high accuracy and speed. However, fully leveraging the capabilities of AI and ML requires the effective and efficient deployment of ML models in production environments. MLOps, a combination of DevOps and ML concepts, aids in managing the lifecycle of ML models from development to deployment and maintenance. In this research, due to international sanctions and limited access to external services such as Google Vertex AI, Amazon SageMaker, and Microsoft Azure ML, a native MLOps platform has been designed and implemented. This platform is developed using open-source tools and provides various features including lifecycle management, performance monitoring, automatic updates, and integration with existing tools. The research aims to address questions related to the needs of a modern MLOps platform and the feasibility of its implementation using open-source tools. The results from the implementation and tests indicate that the proposed platform not only meets modern requirements but also ensures independence from cloud providers. Finally, the findings and recommendations for future research are presented
- Keywords:
- Opensource Development Network ; Open-Source Cloud Platform ; Continuous Integration (CI) ; Continuous Development (CD) ; Machine Learning Operation (MLOps) ; Machine Learning Lifecycle Management
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