Chapter 3: Cloud Training (AWS, GCP, Azure)

Platform Comparison

Feature	AWS SageMaker	GCP Vertex AI	Azure ML
Notebooks	✅	✅	✅
AutoML	✅	✅	✅
Pipelines	✅	✅	✅
Spot/Preemptible	✅	✅	✅
MLflow Integration	✅	✅	✅

AWS SageMaker Example

import sagemaker
from sagemaker.pytorch import PyTorch

estimator = PyTorch(
    entry_point='train.py',
    source_dir='src/',
    role=sagemaker.get_execution_role(),
    instance_count=2,
    instance_type='ml.p3.2xlarge',
    framework_version='2.0',
    py_version='py310',
    hyperparameters={
        'epochs': 10,
        'batch-size': 32,
        'learning-rate': 0.001
    },
    use_spot_instances=True,
    max_wait=3600,  # Max wait time for spot
    checkpoint_s3_uri='s3://bucket/checkpoints/'
)

estimator.fit({'training': 's3://bucket/data/train/'})

Cost Optimization Tips

1. Use spot/preemptible instances - 60-90% savings
2. Right-size instances - Don't over-provision
3. Auto-shutdown notebooks - Avoid idle charges
4. Use checkpointing - Resume interrupted training

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Next: Module 5 Quiz