Cite this DOI

Lakshmi Prasad Rongali (2025). Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids. *Journal of Science & Technology*, *10*(4), 27–33. https://doi.org/10.46243/jst.2025.v10.i04.pp27-33

@article{lakshmiprasadrongali2025utilizing,
  author    = {Lakshmi Prasad Rongali},
  title     = {{Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids.}},
  journal   = {Journal of Science \& Technology},
  year      = {2025},
  month     = {apr},
  volume    = {10},
  number    = {4},
  pages     = {27--33},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2025.v10.i04.pp27-33},
  url       = {https://doi.org/10.46243/jst.2025.v10.i04.pp27-33},
  abstract  = {The research presents an analysis of the enhancement process of AI-driven DevOps in grid management by modifyinganomaly detection, predictive maintenance and entire system effectiveness. It involves an AI driven continuousfeedback loop between these two areas, to get the best delivery and upgrades of the AI models. This collaborationhelps improve system reliability as well as speed up the deployment of needed updates, requiring the smart grid to runas close to optimal as possible.}
}

TY  - JOUR
TI  - Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids.
AU  - Lakshmi Prasad Rongali
JO  - Journal of Science & Technology
PY  - 2025
DA  - 2025/04/21/
VL  - 10
IS  - 4
SP  - 27
EP  - 33
PB  - Longman Publishers
SN  - 2456-5660
AB  - The research presents an analysis of the enhancement process of AI-driven DevOps in grid management by modifyinganomaly detection, predictive maintenance and entire system effectiveness. It involves an AI driven continuousfeedback loop between these two areas, to get the best delivery and upgrades of the AI models. This collaborationhelps improve system reliability as well as speed up the deployment of needed updates, requiring the smart grid to runas close to optimal as possible.
DO  - 10.46243/jst.2025.v10.i04.pp27-33
UR  - https://doi.org/10.46243/jst.2025.v10.i04.pp27-33
ER  -

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    "DOI": "10.46243/jst.2025.v10.i04.pp27-33",
    "URL": "https://doi.org/10.46243/jst.2025.v10.i04.pp27-33",
    "title": "Utilizing AI-Driven DevOps for Predictive Maintenance and Anomaly Detection in Smart Grids.",
    "source": "Smart Scholars DOI Registry",
    "container-title": "Journal of Science & Technology",
    "author": [
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            "family": "Lakshmi Prasad Rongali"
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    "volume": "10",
    "issue": "4",
    "page": "27-33",
    "publisher": "Longman Publishers",
    "abstract": "The research presents an analysis of the enhancement process of AI-driven DevOps in grid management by modifyinganomaly detection, predictive maintenance and entire system effectiveness. It involves an AI driven continuousfeedback loop between these two areas, to get the best delivery and upgrades of the AI models. This collaborationhelps improve system reliability as well as speed up the deployment of needed updates, requiring the smart grid to runas close to optimal as possible.",
    "ISSN": "2456-5660"
}

From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2025.v10.i04.pp27-33 gives all four in one JSON answer.