# Using AntiPatterns to avoid MLOps Mistakes

arXiv: https://arxiv.org/abs/2107.00079 · HTML: https://mlsanity.com/paper/2107.00079/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 48 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 20 of 20)

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Deep Reinforcement Learning that Matters](https://mlsanity.com/paper/1709.06560/) (2017)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty](https://mlsanity.com/paper/2011.07586/) (2020)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [On the State of the Art of Evaluation in Neural Language Models](https://mlsanity.com/paper/1707.05589/) (2017)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Hyperparameter Importance Across Datasets](https://mlsanity.com/paper/1710.04725/) (2017)
- [Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://mlsanity.com/paper/1805.09843/) (2018)
- [Are All Languages Equally Hard to Language-Model?](https://mlsanity.com/paper/1806.03743/) (2018)
- [Bayesian Optimization for Iterative Learning](https://mlsanity.com/paper/1909.09593/) (2019)
- [HARK Side of Deep Learning -- From Grad Student Descent to Automated Machine Learning](https://mlsanity.com/paper/1904.07633/) (2019)
- [On the Importance of Strong Baselines in Bayesian Deep Learning](https://mlsanity.com/paper/1811.09385/) (2018)
- ['Beating the news' with EMBERS: Forecasting Civil Unrest using Open Source Indicators](https://mlsanity.com/paper/1402.7035/) (2014)
- [Hierarchical Quickest Change Detection via Surrogates](https://mlsanity.com/paper/1603.09739/) (2016)

## Built on by (most cited first, 5 of 5)

- [Data-Centric Epidemic Forecasting: A Survey](https://mlsanity.com/paper/2207.09370/) (2022)
- [TinyMLOps: Operational Challenges for Widespread Edge AI Adoption](https://mlsanity.com/paper/2203.10923/) (2022)
- ["Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint](https://mlsanity.com/paper/2201.08246/) (2022)
- [MLSmellHound: A Context-Aware Code Analysis Tool](https://mlsanity.com/paper/2205.03790/) (2022)
- [Data-Driven Radio Propagation Modeling using Graph Neural Networks](https://mlsanity.com/paper/2501.06236/) (2025)

## Abstract (arXiv)

We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are presented in the form of antipatterns. Just as design patterns codify best software engineering practices, antipatterns provide a vocabulary to describe defective practices and methodologies. Here we catalog and document numerous antipatterns in financial ML operations (MLOps). Some antipatterns are due to technical errors, while others are due to not having sufficient knowledge of the surrounding context in which ML results are used. By providing a common vocabulary to discuss these situations, our intent is that antipatterns will support better documentation of issues, rapid communication between stakeholders, and faster resolution of problems. In addition to cataloging antipatterns, we describe solutions, best practices, and future directions toward MLOps maturity.