S3 and object storage specialists for your dedicated team.

Where S3 and object storage fits in a long-term product team.

Object storage is the substrate under most machine learning infrastructure. Training data, checkpoints and model artefacts all live there, and the durability and cost profile make it the right default for anything that is written once and read many times.

Key layout matters more than it appears. A prefix structure that supports the access patterns, such as listing a dataset version or retrieving a specific checkpoint, decides whether operations are efficient or need expensive listings, and it is very hard to reorganise once terabytes are stored.

What your dedicated team does with S3 and object storage.

Training throughput is often limited by storage. A GPU waiting for data is expensive idle time, and the fix is usually parallel reads, prefetching or a local cache.

Diagnosing that correctly takes measuring where the time goes, the kind of rigour held within an agreed committed monthly capacity.

Common S3 and object storage use cases.

• Artefacts stored durably and cheaply Datasets, checkpoints and weights on the right substrate for write-once read-many.
• Key layout matched to access Prefixes supporting the real operations, because reorganising later is impractical.
• Storage removed as the training bottleneck Prefetching and caching so the GPU is not idle waiting for data.

Adding S3 and object storage skills to your team.

Artefact storage is assigned across AI and data capacity, with throughput measured before compute is increased.

At Azendo, S3 and object storage work sits with a dedicated MLOps team. Every S3 and object storage specialist we assign works remotely inside your tools as a dedicated member of your team, with the management on our side. It is how companies hire remote developers without opening an offshore office of their own.