# AI Total: Analyzing Security ML Models with Imperfect Data in Production

arXiv: https://arxiv.org/abs/2110.07028 · HTML: https://mlsanity.com/paper/2110.07028/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features](https://mlsanity.com/paper/1508.03096/) (2015)
- [Engineering AI Systems: A Research Agenda](https://mlsanity.com/paper/2001.07522/) (2020)
- [A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?](https://mlsanity.com/paper/2012.03061/) (2020)
- [DeepDGA: Adversarially-Tuned Domain Generation and Detection](https://mlsanity.com/paper/1610.01969/) (2016)
- [FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches](https://mlsanity.com/paper/2103.14539/) (2021)

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

- [Understanding Practitioners Perspectives on Monitoring Machine Learning Systems](https://mlsanity.com/paper/2509.25195/) (2025)

## Abstract (arXiv)

Development of new machine learning models is typically done on manually curated data sets, making them unsuitable for evaluating the models' performance during operations, where the evaluation needs to be performed automatically on incoming streams of new data. Unfortunately, pure reliance on a fully automatic pipeline for monitoring model performance makes it difficult to understand if any observed performance issues are due to model performance, pipeline issues, emerging data distribution biases, or some combination of the above. With this in mind, we developed a web-based visualization system that allows the users to quickly gather headline performance numbers while maintaining confidence that the underlying data pipeline is functioning properly. It also enables the users to immediately observe the root cause of an issue when something goes wrong. We introduce a novel way to analyze performance under data issues using a data coverage equalizer. We describe the various modifications and additional plots, filters, and drill-downs that we added on top of the standard evaluation metrics typically tracked in machine learning (ML) applications, and walk through some real world examples that proved valuable for introspecting our models.