Object Storage for AI Data Pipelines
Using S3 as the central data layer for machine learning workflows: storing training data, model checkpoints, feature stores, embeddings, and model artifacts in object storage.
Summary
Using S3 as the central data layer for machine learning workflows: storing training data, model checkpoints, feature stores, embeddings, and model artifacts in object storage.
As ML/AI workloads scale, S3 becomes the gravitational center for all data assets in the pipeline. Object storage provides the durability, scale, and accessibility that ML workflows need — from raw training data to production model serving.
- S3 is not a high-performance training data source out of the box. Naive sequential reads from S3 during GPU training leave GPUs idle. Prefetching, caching, and streaming libraries are required.
- Checkpoint storage on S3 is durable but slow to write. Large model checkpoints (tens of GB) require parallel multipart uploads and careful error handling.
scoped_toS3, Object Storage — S3 as the data backbone for ML- Training Data Streaming from Object Storage
scoped_toObject Storage for AI Data Pipelines — streaming pattern - Checkpoint/Artifact Lake on Object Storage
scoped_toObject Storage for AI Data Pipelines — durable checkpoint storage - Feature/Embedding Store on Object Storage
scoped_toObject Storage for AI Data Pipelines — feature and embedding persistence - GeeseFS
scoped_toObject Storage for AI Data Pipelines — POSIX access for ML frameworks
Definition
Using S3-compatible object storage as the central data layer for ML workflows — training data staging, checkpoint persistence, model artifact management, and feature/embedding storage.
AI/ML workloads generate and consume massive volumes of unstructured data. Object storage provides the durability, scalability, and HTTP accessibility needed for distributed training.
Connections 10
Outbound 3
Resources 3
AWS Storage Blog describing how S3 serves as the backbone for AI/RAG data pipelines with EKS and S3 Vectors.
NVIDIA blog on efficient GPU training data loading directly from S3 using the DALI S3 plugin.
SageMaker documentation on accessing training data from S3, covering input modes and data channels.