Design of Proprietary Frameworks for Neural Models: Methodology and Best Practices
DOI:
https://doi.org/10.69987/JACS.2024.40803Keywords:
Proprietary frameworks, neural models, Artificial intelligence, framework design, model optimization, continuous evaluation, data management, model training, regulatory compliance, agile methodology, security and privacyAbstract
The creation of proprietary frameworks for the development of neural models is essential to meet specific needs that generic frameworks cannot address. This article examines the key stages in the design of these frameworks and offers best practices for their effective implementation. It explores everything from needs identification and resource assessment to architectural design and implementation. Additionally, it emphasizes the importance of user-centered design and continuous evaluation to ensure the framework's usability and adaptability to changing needs.
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Copyright (c) 2024 © 2024 Journal of Advanced Computing Systems (JACS). All rights reserved. Authors retain copyright and grant JACS right of first publication under a Creative Commons Attribution License.
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