The machine learning platform for developers of large systems
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866909616673128448 |
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| author | Naikov, Alexey Oreshkin, Anatoly Shvetsov, Alexey Shevel, Andrey |
| author_facet | Naikov, Alexey Oreshkin, Anatoly Shvetsov, Alexey Shevel, Andrey |
| contents | The machine learning system in the form of Retrieval Augmented Generation (RAG) has developed steadily since about 2021. RAG could be observed as a version of the knowledge transfer. In the studied case, the large computing systems are observed as the application point of RAG, which includes large language model (LLM), as a partner for the developing team. Such an approach has advantages during the development process and further in exploitation time. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The machine learning platform for developers of large systems Naikov, Alexey Oreshkin, Anatoly Shvetsov, Alexey Shevel, Andrey General Physics J.2 The machine learning system in the form of Retrieval Augmented Generation (RAG) has developed steadily since about 2021. RAG could be observed as a version of the knowledge transfer. In the studied case, the large computing systems are observed as the application point of RAG, which includes large language model (LLM), as a partner for the developing team. Such an approach has advantages during the development process and further in exploitation time. |
| title | The machine learning platform for developers of large systems |
| topic | General Physics J.2 |
| url | https://arxiv.org/abs/2501.13881 |