Deploying automatic speech recognition system for scalability, reliability, and security with Kubernetes
This project undertakes the task of refining the existing Automatic Speech Recognition (ASR) system’s deployment, orchestrated by Kubernetes on the cloud. While focusing on reliability, scalability, and security, it also aspires to maintain a balanced approach to cost-effectiveness. In pursuit of...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/171933 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This project undertakes the task of refining the existing Automatic Speech Recognition
(ASR) system’s deployment, orchestrated by Kubernetes on the cloud. While focusing
on reliability, scalability, and security, it also aspires to maintain a balanced approach
to cost-effectiveness. In pursuit of enhanced data and processing reliability, Apache
Kafka has been considered. For bolstering security measures, the incorporation of
technologies such as Kyverno and Falco has been explored. Kyverno serves to enforce
adherence to essential cluster rules, aiming to mitigate human-induced discrepancies,
whereas Falco is introduced with a vision to provide cluster system administrators with
potential insights into any unforeseen malicious activities. Beyond these solutions, the
report also examines other elements that play pivotal roles in enhancing the overall
architecture. The document seeks to elaborate on these modifications, offering a
detailed perspective on how each element collaboratively contributes to the system’s
advancement. |
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