Abstract

As cloud storage grows in size and complexity, the challenge of keeping costs under control becomes more urgent. Traditional storage management relies on static rules and manual analysis, but these approaches struggle to keep up with today’s dynamic, data-driven environments. AI and machine learning (ML) are now being used to analyze how data is accessed, predict future costs, and recommend the most cost-effective storage tiers and configurations. 

This article walks through the process of building a simple machine learning model in Python to predict S3 storage costs and suggest optimal storage classes. Along the way, you’ll see what’s required to get started, the practical value of ML in cloud storage, and lessons learned from real-world deployments.