Navigating Time Constraints in Profit Model Retraining

Jorrick Stempher shares how his team of eight students built a complete predictive scaling system for Kubernetes clusters using machine learning.

Rather than waiting for nodes to become overloaded, their system uses the Prophet forecasting model to proactively anticipate load patterns and scale infrastructure, giving them the 8-9 minutes needed to provision new nodes on Vultr.

You will learn:

• How to implement predictive scaling using Prophet ML model, Prometheus metrics, and custom APIs to forecast Kubernetes workload patterns

• The Node Ranking Index (NRI) - a unified metric that combines CPU, RAM, and request data into a single comparable number for efficient scaling decisions

• Real-world implementation challenges, including data validation, node startup timing constraints, load testing strategies, and the importance of proper research before building complex scaling solutions

Sponsor

This episode is brought to you by Testkube—the ultimate Continuous Testing Platform for Cloud Native applications. Scale fast, test continuously, and ship confidently. Check it out at testkube.io

More info

• Find all the links and info for this episode here: https://ku.bz/clbDWqPYp

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