# Development of an End-to-end Machine Learning System with Application to In-app Purchases

arXiv: https://arxiv.org/abs/2412.12390 · HTML: https://mlsanity.com/paper/2412.12390/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Continual Learning in Practice](https://mlsanity.com/paper/1903.05202/) (2019)
- [Looper: An end-to-end ML platform for product decisions](https://mlsanity.com/paper/2110.07554/) (2021)
- [Scalable End-to-End ML Platforms: from AutoML to Self-serve](https://mlsanity.com/paper/2302.14139/) (2023)

## Abstract (arXiv)

Machine learning (ML) systems have become vital in the mobile gaming industry. Companies like King have been using them in production to optimize various parts of the gaming experience. One important area is in-app purchases: purchases made in the game by players in order to enhance and customize their gameplay experience. In this work we describe how we developed an ML system in order to predict when a player is expected to make their next in-app purchase. These predictions are used to present offers to players. We briefly describe the problem definition, modeling approach and results and then, in considerable detail, outline the end-to-end ML system. We conclude with a reflection on challenges encountered and plans for future work.