Storage Foundations for ML Systems
Explore the foundational AWS storage services essential for machine learning workflows, including Amazon S3, EFS, FSx, and EBS. Understand how to select the right storage based on access patterns, cost, performance, and security for training and deployment on AWS. Gain practical insights into optimizing data transfer, encryption, and lifecycle management to build efficient and secure ML systems.
A poorly chosen storage layer can bottleneck distributed training, inflate costs for unused data, or expose sensitive datasets to unauthorized access. This lesson maps core AWS storage services to the ML pipeline stages they support and builds the trade-off reasoning that the exam expects.
Four storage services form the backbone of ML workloads on AWS. Amazon S3 provides object storage for datasets, model artifacts, and training logs. Amazon Elastic File System (EFS) enables shared file access across multiple training instances. Amazon FSx for NetApp ONTAP delivers high-performance NFS/SMB access for workloads migrating from on-premises environments or requiring low latency. Amazon FSx for Lustre provides high-throughput file storage for ML training workloads and integrates with S3.
Consider a practical scenario throughout this lesson: An ML engineer must ingest a multi-terabyte training dataset, serve it efficiently to SageMaker distributed training jobs, and store the resulting model artifacts for deployment, all while keeping costs low and data encrypted.
The following diagram illustrates how data flows through these storage services across the ML life cycle.
With this life cycle in mind, let’s examine each storage service in detail, starting with the one you’ll encounter most frequently on the exam.
Amazon S3 as the ML data backbone
Amazon S3 is the default storage service for ML on AWS. SageMaker natively reads training input from S3 URIs, writes model artifacts back to S3, and persists training checkpoints in S3. This tight integration means that nearly every SageMaker pipeline begins and ends with S3.
Storage classes and cost optimization
Not all training data requires the same access frequency, and S3 storage classes let you align cost with usage patterns.
S3 Standard serves active training datasets that SageMaker jobs read repeatedly during experimentation cycles.
S3 Intelligent-Tiering automatically moves objects between frequent- and infrequent-access tiers, making it well suited for datasets with unpredictable ...