Talk

This talk will challenge the traditional practice of manually setting static requests and limits for Kubernetes workloads. We’ll start with the foundations - how resource requests work, why it isn’t easy to get them right, and the real-world consequences of misconfiguration. From there, we’ll explore how data-driven approaches and tools like the VPA can enhance resource management through machine learning techniques, bringing intelligence to what has traditionally been guesswork.

🔥 What you’ll learn:

• How data-driven approaches can dynamically optimize resource allocation, saving costs while preventing performance degradation
• The relationship between resource settings and infrastructure sizing and how intelligent adjustments can dramatically improve efficiency
• When to apply limits vs. when to let algorithms handle scaling decisions
• Practical implementation strategies for both simple and advanced machine learning techniques in your Kubernetes environment

Who should attend:

DevOps engineers, SREs, platform engineers, and technical leaders looking to leverage AI to solve one of the most persistent challenges in Kubernetes management.