POSIX Filesystems on Object Storage: The Good, the Bad, the Fast
POSIX semantics on top of object storage is an old and messy problem. Here's what's possible, what's impossible, and what ML teams should actually demand from a storage layer.
Mounting S3 as NFS: Why FUSE Isn't Enough for Production
Searching for 'mount S3 as NFS' turns up a dozen FUSE-based tools. Here's why none of them survive production ML workloads, and what actually works.
AWS EFS vs Training Pipes: A Cost Breakdown for ML Workloads
EFS gives you POSIX on AWS. Training Pipes gives you POSIX plus caching plus any cloud. We ran the numbers on a realistic ML training workload to see which wins.
Stop Using s3fs in Production: Better Alternatives for ML Teams
s3fs-fuse is a fine prototype tool and a dangerous production dependency. Here's what breaks, why, and what to use instead for real ML training workloads.
NFS vs S3 for AI Training: When to Use Each
NFS and S3 solve different problems — but AI teams have to use both. Here's a clear framework for when each protocol wins, and how to stop choosing between them.