# MLOps -- Definitions, Tools and Challenges

arXiv: https://arxiv.org/abs/2201.00162 · HTML: https://mlsanity.com/paper/2201.00162/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 1 Jan 2022.

## Builds on (most cited first, 10 of 10)

- [Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning](https://mlsanity.com/paper/2007.04074/) (2020)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL](https://mlsanity.com/paper/2006.13799/) (2020)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?](https://mlsanity.com/paper/2103.08942/) (2021)
- [A Data Quality-Driven View of MLOps](https://mlsanity.com/paper/2102.07750/) (2021)
- [Monitoring and explainability of models in production](https://mlsanity.com/paper/2007.06299/) (2020)
- [MLOps Challenges in Multi-Organization Setup: Experiences from Two Real-World Cases](https://mlsanity.com/paper/2103.08937/) (2021)
- [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://mlsanity.com/paper/2109.03947/) (2021)
- [The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps](https://mlsanity.com/paper/2006.07161/) (2020)

## Built on by (most cited first, 25 of 25)

- [MLOps: A Review](https://mlsanity.com/paper/2308.10908/) (2023)
- [DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systems](https://mlsanity.com/paper/2211.05764/) (2022)
- [Machine Learning with Requirements: a Manifesto](https://mlsanity.com/paper/2304.03674/) (2023)
- [MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis](https://mlsanity.com/paper/2309.15521/) (2023)
- [OceanBench: The Sea Surface Height Edition](https://mlsanity.com/paper/2309.15599/) (2023)
- [Architecting Digital Twins for Intelligent Transportation Systems](https://mlsanity.com/paper/2502.17646/) (2025)
- [Securing RAG: A Risk Assessment and Mitigation Framework](https://mlsanity.com/paper/2505.08728/) (2025)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [MLOps: A Step Forward to Enterprise Machine Learning](https://mlsanity.com/paper/2305.19298/) (2023)
- [Towards an MLOps Architecture for XAI in Industrial Applications](https://mlsanity.com/paper/2309.12756/) (2023)
- [Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines](https://mlsanity.com/paper/2302.04977/) (2023)
- [Extending Variability-Aware Model Selection with Bias Detection in Machine Learning Projects](https://mlsanity.com/paper/2311.14214/) (2023)
- [Backward Compatibility in Attributive Explanation and Enhanced Model Training Method](https://mlsanity.com/paper/2408.02298/) (2024)
- [Machine Learning Operations: A Mapping Study](https://mlsanity.com/paper/2409.19416/) (2024)
- [End-to-End Data Quality-Driven Framework for Machine Learning in Production Environment](https://mlsanity.com/paper/2512.19723/) (2025)
- [A Data Source Dependency Analysis Framework for Large Scale Data Science Projects](https://mlsanity.com/paper/2212.07951/) (2022)
- [Good Tools are Half the Work: Tool Usage in Deep Learning Projects](https://mlsanity.com/paper/2310.19124/) (2023)
- [Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities](https://mlsanity.com/paper/2312.03812/) (2023)
- [Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review](https://mlsanity.com/paper/2312.06697/) (2023)
- [Automating Code Adaptation for MLOps -- A Benchmarking Study on LLMs](https://mlsanity.com/paper/2405.06835/) (2024)
- [HarmonE: A Self-Adaptive Approach to Architecting Sustainable MLOps](https://mlsanity.com/paper/2505.13693/) (2025)
- [Getting In Contract with Large Language Models -- An Agency Theory Perspective On Large Language Model Alignment](https://mlsanity.com/paper/2509.07642/) (2025)
- [Embedding the MLOps Lifecycle into OT Reference Models](https://mlsanity.com/paper/2510.20590/) (2025)
- [Model Gateway: Management Platform for Model-Driven Drug Discovery](https://mlsanity.com/paper/2512.05462/) (2025)
- [Trustworthy Data- and ML-Ops for Intelligent Transportation Systems and Logistics](https://mlsanity.com/paper/2610.01282/) (2026)

## Abstract (arXiv)

This paper is an overview of the Machine Learning Operations (MLOps) area. Our aim is to define the operation and the components of such systems by highlighting the current problems and trends. In this context, we present the different tools and their usefulness in order to provide the corresponding guidelines. Moreover, the connection between MLOps and AutoML (Automated Machine Learning) is identified and how this combination could work is proposed.