Machine-learning predictive autoscaling for Flink

This blog post discusses Grab's innovative approach to optimizing resource allocation for Flink applications through machine learning-based predictive autoscaling. By analyzing the challenges posed by reactive autoscaling and creating a predictive resource advisor, Grab has improved CPU provisioning, reduced costs, and streamlined the deployment process for users. The solution is set to enhance stability, efficiency, and user experience while also planning for future improvements in memory configuration and model accuracy.