# On-Premise AIOps Infrastructure for a Software Editor SME: An Experience Report

arXiv: https://arxiv.org/abs/2308.11225 · HTML: https://mlsanity.com/paper/2308.11225/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 41 references that are in the map.
- First posted on arXiv 22 Aug 2023.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Time-Series Anomaly Detection Service at Microsoft](https://mlsanity.com/paper/1906.03821/) (2019)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Towards a consistent interpretation of AIOps models](https://mlsanity.com/paper/2202.02298/) (2022)
- [A Systematic Mapping Study in AIOps](https://mlsanity.com/paper/2012.09108/) (2020)
- [Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper](https://mlsanity.com/paper/2101.06054/) (2021)
- [Interpretable Summaries of Black Box Incident Triaging with Subgroup Discovery](https://mlsanity.com/paper/2108.03013/) (2021)
- [SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things](https://mlsanity.com/paper/2204.09795/) (2022)

## Built on by (most cited first, 1 of 1)

- [Exploiting Formal Concept Analysis for Data Modeling in Data Lakes](https://mlsanity.com/paper/2408.13265/) (2024)

## Abstract (arXiv)

Information Technology has become a critical component in various industries, leading to an increased focus on software maintenance and monitoring. With the complexities of modern software systems, traditional maintenance approaches have become insufficient. The concept of AIOps has emerged to enhance predictive maintenance using Big Data and Machine Learning capabilities. However, exploiting AIOps requires addressing several challenges related to the complexity of data and incident management. Commercial solutions exist, but they may not be suitable for certain companies due to high costs, data governance issues, and limitations in covering private software. This paper investigates the feasibility of implementing on-premise AIOps solutions by leveraging open-source tools. We introduce a comprehensive AIOps infrastructure that we have successfully deployed in our company, and we provide the rationale behind different choices that we made to build its various components. Particularly, we provide insights into our approach and criteria for selecting a data management system and we explain its integration. Our experience can be beneficial for companies seeking to internally manage their software maintenance processes with a modern AIOps approach.