5.5.4. Data Storage and Transfer Costs

First Principle: Optimizing data storage and transfer costs fundamentally involves selecting appropriate storage classes, minimizing unnecessary data movement, and leveraging private network options to reduce expenditure for large-scale ML datasets.

While compute costs often dominate ML budgets, data storage and transfer can also contribute significantly, especially with large datasets and frequent data movement.

Key Strategies for Optimizing Data Storage and Transfer Costs in ML:

• Amazon S3 Storage Classes:
  • Purpose: Choose the right S3 storage class based on data access patterns and durability requirements to optimize costs.
  • S3 Standard: For frequently accessed data. Highest cost per GB, but lowest retrieval cost.
  • S3 Intelligent-Tiering: Automatically moves data between two access tiers (frequent and infrequent) based on access patterns. Good for data with unknown or changing access patterns.
  • S3 Standard-IA (Infrequent Access): For infrequently accessed data that requires rapid access when needed. Lower storage cost, but higher retrieval cost.
  • S3 One Zone-IA: Same as Standard-IA but stored in a single Availability Zone. Lower cost, but less durable (data can be lost if AZ fails).
  • S3 Glacier Flexible Retrieval: For archival data that is rarely accessed, with retrieval times from minutes to hours. Very low storage cost.
  • S3 Glacier Deep Archive: Lowest cost storage, for long-term archives accessed once or twice a year, with retrieval times in hours.
  • Use Cases for ML:
    • Raw Data Lake: Start with Intelligent-Tiering or Standard-IA.
    • Processed Training Data: Standard or Standard-IA.
    • Model Artifacts: Standard (frequently accessed for deployment) or Standard-IA (for older versions).
    • Archival Data: Glacier for old, rarely needed datasets.
• Minimize Data Transfer Costs (Egress):
  • Data Transfer Out of AWS (Egress): This is typically the most expensive data transfer. Design your architecture to minimize data leaving the AWS network.
  • Cross-Region Data Transfer: Transferring data between different AWS Regions is also more expensive than within a Region. Keep data and compute in the same Region if possible.
  • VPC Endpoints: Use VPC Endpoints for Amazon S3 and other AWS services to keep traffic within the AWS network, avoiding internet egress charges and enhancing security.
  • Data Locality: Process data where it resides. For example, use SageMaker Processing Jobs or AWS Glue to transform data directly in S3, rather than downloading it to an on-premises environment and re-uploading.
• Data Lifecycle Management:
  • S3 Lifecycle Policies: Automatically transition objects between storage classes or expire them after a certain period, based on predefined rules. This helps automate cost optimization for data that ages.
• Delete Unused Data/Models: Regularly clean up old, unused datasets, intermediate files, and deprecated model artifacts from S3.

Scenario: Your data lake contains petabytes of historical log data that is rarely accessed after the first 30 days. You also have large training datasets that are frequently accessed during model development but become less frequent after a few weeks. You need to store these cost-effectively and minimize data transfer costs when SageMaker accesses them.

Reflection Question: How does optimizing data storage and transfer costs (e.g., using S3 Intelligent-Tiering or S3 Standard-IA for varying access patterns, leveraging S3 Lifecycle Policies, and utilizing VPC Endpoints for S3) fundamentally reduce expenditure for large-scale ML datasets by selecting appropriate storage classes and minimizing unnecessary data movement?

💡 Tip: Data egress is expensive. Always design your architecture to keep data processing and ML workloads within the AWS network and in the same Region as your data.