Scaling and Automating High-Performance ML Pipelines with FSx on EKS

So far, you’ve built a ML training architecture on EKS using FSx for Lustre, optimized it for high-throughput I/O, and validated that it supports large-scale, stateful workloads. But to operate this system in a production environment—where multiple experiments run simultaneously, datasets shift dynamically, and training needs fluctuate—you need automation, scalability, and cost efficiency.