AI/ML Infrastructure on AWS: A Production-Ready Blueprint

Source: Dev.to

High‑Throughput Training Data Storage

# Create FSx for Lustre linked to S3 training data
aws fsx create-file-system \
  --file-system-type LUSTRE \
  --storage-capacity 1200 \
  --lustre-configuration ImportPath=s3://training-data-bucket

FSx for Lustre delivers 100+ GB/s throughput versus S3’s ~5 GB/s. A job that takes 8 hours on S3 can finish in ~45 minutes on Lustre.

GPU Node Provisioning with Karpenter

apiVersion: karpenter.sh/v1alpha5
kind: Provisioner
metadata:
  name: gpu-training
spec:
  requirements:
    - key: node.kubernetes.io/instance-type
      operator: In
      values: ["p4d.24xlarge", "p3.8xlarge", "g5.12xlarge"]
    - key: karpenter.sh/capacity-type
      operator: In
      values: ["spot", "on-demand"]
  limits:
    resources:
      nvidia.com/gpu: 32

• Spot GPU instances can reduce costs by 60–70 %.
• Karpenter automatically provisions the appropriate GPU type based on the workload.

Deploying a SageMaker Model with Auto‑Scaling

import sagemaker
from sagemaker.model import ModelPackage

model_package = ModelPackage(
    model_package_arn="arn:aws:sagemaker:us-east-1:123456:model-package/my-model/1",
    role=sagemaker_role,
    sagemaker_session=session
)

# Deploy with auto‑scaling
predictor = model_package.deploy(
    initial_instance_count=2,
    instance_type="ml.g5.xlarge",
    endpoint_name="production-inference"
)

Hosting Multiple Models on a Single Endpoint

from sagemaker.multidatamodel import MultiDataModel

mme = MultiDataModel(
    name="multi-model-endpoint",
    model_data_prefix=f"s3://{bucket}/models/",
    model=model,
    sagemaker_session=session
)

Running 10+ models on a single endpoint can significantly cut inference costs.

Data and Model Drift Monitoring

from sagemaker.model_monitor import DataCaptureConfig

data_capture = DataCaptureConfig(
    enable_capture=True,
    sampling_percentage=20,
    destination_s3_uri=f"s3://{bucket}/capture"
)

Enable data capture to monitor:

• Data drift
• Model drift
• Changes in feature importance

Additional Resources

• AI/ML Toolkits – 40+ Terraform modules, pipeline templates, and deployment blueprints: AI/ML Toolkits
• Architecture Blueprints – Production‑ready ML architecture patterns: Architecture Blueprints
• Free AI/ML Course – Learn the fundamentals at no cost: Free Courses

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