Practical Machine Learning Models to prevent Revenue Loss

2026 Apr 30

Practical Machine Learning Models to prevent Revenue Loss

In this presentation, we offer a demonstration of machine learning (ML) to create an intelligent application based on distributed system data. We’ll show ML techniques in the development of a data analysis application to monitor distributed platforms with a direct impact on company revenue. Additionally, we provide the source code for a practical demonstration of how to train ML models and perform predictions with Apache Spark.

Overview

Traditional monitoring often focuses on infrastructure metrics (CPU, Memory, Disk), but it can miss subtle logical failures or integration issues that lead to revenue leakage. This talk covers how we built “Watch AI” at Movile to bridge this gap using predictive modeling.

Watch the Presentation

You can find the full presentation from InfoQ Brazil below:

And here is the version presented at Spark + AI Summit Europe:

Presentation Slides

The slides for this presentation are available on SlideShare:

Preventing Revenue Leakage and Monitoring Distributed Systems with Machine Learning from Spark Summit

Key Takeaways

  • Business Integrity Monitoring: Why monitoring business metrics is as important as monitoring infrastructure.
  • Predictive Analysis: Using Spark MLlib (Linear Regression, Decision Trees) to predict expected transaction volumes.
  • Real-time Alerts: Reducing “Time to Action” (TTA) from hours to minutes.
  • Impact: Preventing millions in potential revenue loss by identifying integration failures early.

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