1.4.1. Foundational Services (Compute, Storage, Networking)

First Principle: Foundational AWS services provide the essential building blocks for machine learning workloads, offering scalable compute power, durable storage, and secure networking, underpinning the entire ML lifecycle.

While AWS offers specialized ML services, many ML workloads heavily rely on core AWS infrastructure services for compute, storage, and networking. Understanding these foundational services is crucial.

Key Foundational AWS Services for ML:

• Compute:
  • Amazon EC2 (Elastic Compute Cloud): (Provides scalable compute capacity.) Raw compute instances for running ML frameworks, custom training, or self-managed inference. Offers a wide range of instance types, including GPU-accelerated instances (P, G, Inf instances) for deep learning.
  • AWS Lambda: (Serverless compute for event-driven functions.) For lightweight, event-driven inference (e.g., small models triggered by S3 uploads).
  • Amazon EKS (Elastic Kubernetes Service) / Amazon ECS (Elastic Container Service): (Managed container orchestration services.) For containerized ML workloads, custom inference services, or MLOps pipelines.
• Storage:
  • Amazon S3 (Simple Storage Service): (Scalable object storage.) The primary data lake for raw and processed ML data, model artifacts, and training logs. Highly durable and available.
  • Amazon EBS (Elastic Block Store): (Block storage for EC2 instances.) Persistent block-level storage volumes for EC2 instances, often used for model checkpoints or large datasets during training.
  • Amazon FSx for Lustre / FSx for NetApp ONTAP: (High-performance file systems.) For ML workloads requiring very high-throughput, low-latency shared file access (e.g., distributed training).
• Networking:
  • Amazon VPC (Virtual Private Cloud): (Logically isolated virtual network.) Provides network isolation for ML instances, allowing private connectivity to data sources and other AWS services.
  • VPC Endpoints: Private access to AWS services (like S3 or SageMaker APIs) from within your VPC without traversing the public internet.
  • Security Groups: Control network access to/from ML instances.

Scenario: You need to train a large deep learning model on a dataset stored in Amazon S3 and then deploy it for real-time inference. You require high-performance compute and secure, private access to your data and ML services.

Reflection Question: How do foundational AWS services (EC2 for compute, S3 for storage, VPC for networking) fundamentally provide the essential building blocks for machine learning workloads, underpinning the entire ML lifecycle and ensuring scalability, durability, and security?