mlsanityData to 14 Oct 2026

LLM and Infrastructure as a Code use case

arXiv 2309.01456 ↗First posted 4 Sep 2023cs.CLcited by 0

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Abstract (arXiv)

Cloud computing and the evolution of management methodologies such as Lean Management or Agile entail a profound transformation in both system construction and maintenance approaches. These practices are encompassed within the term "DevOps." This descriptive approach to an information system or application, alongside the configuration of its constituent components, has necessitated the development of descriptive languages paired with specialized engines for automating systems administration tasks. Among these, the tandem of Ansible (engine) and YAML (descriptive language) stands out as the two most prevalent tools in the market, facing notable competition mainly from Terraform. The current document presents an inquiry into a solution for generating and managing Ansible YAML roles and playbooks, utilizing Generative LLMs (Language Models) to translate human descriptions into code. Our efforts are focused on identifying plausible directions and outlining the potential industrial applications. Note: For the purpose of this experiment, we have opted against the use of Ansible Lightspeed. This is due to its reliance on an IBM Watson model, for which we have not found any publicly available references. Comprehensive information regarding this remarkable technology can be found [1] directly on our partner's website, RedHat.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 4 Sep 2023.

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6 of 15 references.

References · 6

  1. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,621
  2. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,919
  3. 2020GLU Variants Improve Transformercited by 1,804
  4. 2020Transformer Feed-Forward Layers Are Key-Value Memoriescited by 1,336
  5. 2021Shuffle Transformer: Rethinking Spatial Shuffle for Vision Transformercited by 115
  6. 2019Augmenting Self-attention with Persistent Memorycited by 93

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