5.5. Cost Optimization for ML

First Principle: Cost optimization for ML fundamentally involves judicious selection of instance types, leveraging flexible pricing models (e.g., Spot), and right-sizing resources to maximize compute efficiency and minimize expenditure throughout the ML workflow.

Machine learning workloads can be expensive due to the significant compute and storage resources they consume. Optimizing costs is a critical responsibility for ML specialists to ensure the financial viability and scalability of ML initiatives.

Key Strategies for Cost Optimization for ML:

• Instance Type and Family Selection:
  • Purpose: Choose the most appropriate EC2 instance type for your workload.
  • Considerations:
    • CPU vs. GPU: GPU instances (e.g., P, G, Inf instances) are essential for deep learning but are significantly more expensive. Use them only when necessary.
    • Memory vs. Compute: Select instances with sufficient memory for your data size, and adequate CPU/GPU for computation.
    • Storage Optimized: For data processing that is I/O bound.
    • Inference Optimized: Inf1 and Inf2 instances for high-performance, low-cost inference.
  • AWS: SageMaker allows you to specify instance types for notebooks, training jobs, and endpoints.
• Leveraging Flexible Pricing Models:
  • Managed Spot Training: For SageMaker training jobs. Significantly reduces training costs (up to 90% savings) by using EC2 Spot Instances (unused EC2 capacity) for fault-tolerant workloads.
  • EC2 Spot Instances: Use for self-managed EC2/EMR clusters or custom inference solutions where interruptions are acceptable.
  • Reserved Instances (RIs) / Savings Plans: For stable, long-running workloads (e.g., persistent SageMaker endpoints, EMR clusters) where you can commit to a certain usage level for a discount.
• Right-sizing and Auto Scaling:
  • Right-sizing: Continuously analyze resource utilization to ensure you are using the smallest effective instance type and count.
  • Auto Scaling: Configure SageMaker Endpoints to automatically scale up/down based on traffic patterns to save costs during idle periods.
• Data Storage and Transfer Costs:
  • S3 Storage Classes: Use appropriate classes (Standard, Infrequent Access, Glacier) for different access patterns.
  • Data Transfer (Egress): Minimize data transfer out of AWS (egress) or between Regions/AZs, as this is typically the most expensive. Use VPC Endpoints for private access to S3 and other AWS services.
• Deleting Unused Resources: Stop/terminate unused notebooks, training jobs, or endpoints.

Scenario: You are managing a team of data scientists who are frequently running experiments and training deep learning models. Your AWS bill for compute and inference is very high. You also have a real-time inference endpoint that experiences highly variable traffic throughout the day.

Reflection Question: How does cost optimization for ML (e.g., leveraging Managed Spot Training for experiments, selecting appropriate instance types, implementing auto-scaling for endpoints, minimizing data egress) fundamentally involve judicious selection of resources and pricing models to maximize compute efficiency and minimize expenditure throughout the ML workflow?