AWS AI & SageMaker

Amazon Web Services is the world's largest cloud provider, with the most mature and comprehensive set of AI/ML services. SageMaker is its flagship ML platform — handling everything from data preparation to model training, deployment, and monitoring.

AWS AI Service Landscape

🏋️

SageMaker

End-to-end ML platform: notebooks, training, deployment, monitoring

🤖

Bedrock

Managed LLM API: Claude, Llama, Titan, Stable Diffusion. No GPU management.

👁️

Rekognition

Pre-built computer vision: object detection, face recognition, content moderation

📝

Comprehend

NLP service: sentiment, entities, key phrases, language detection

🗣️

Transcribe / Polly

Speech-to-text and text-to-speech services

🔄

Glue / Athena

Data preparation and SQL analytics on S3 data lakes

SageMaker: The ML Lifecycle

SageMaker Studio

Web-based IDE. Jupyter notebooks with managed compute. No local setup.

Data Wrangler

Visual data preparation: clean, transform, and visualise S3 data.

Training Jobs

Managed container-based training. Auto-provisions GPUs, handles checkpointing.

Experiments

Track hyperparameters, metrics, and artefacts across training runs.

Model Registry

Version models, track lineage, approve/reject for deployment.

Endpoints

Deploy models as real-time or batch REST APIs. Auto-scaling built-in.

Model Monitor

Detect data drift and model quality degradation in production.

Launch a Training Job

Python · SageMaker Training Job (PyTorch)

import sagemaker
from sagemaker.pytorch import PyTorch

role = sagemaker.get_execution_role()
sess = sagemaker.Session()

# Upload training data to S3
s3_data = sess.upload_data('data/', bucket='my-bucket', key_prefix='training')

# Define estimator
estimator = PyTorch(
    entry_point='train.py',          # Your training script
    role=role,
    framework_version='2.1',
    py_version='py310',
    instance_type='ml.p3.2xlarge',  # V100 GPU, $3.82/hr
    instance_count=1,
    hyperparameters={
        'epochs': 50,
        'learning-rate': 0.001,
    }
)

# Start training (provisions GPU, runs, saves model to S3)
estimator.fit({'training': s3_data})

Deploy a Model Endpoint

Python · SageMaker Real-Time Endpoint

# Deploy trained model as REST API
predictor = estimator.deploy(
    initial_instance_count=1,
    instance_type='ml.c5.xlarge',  # CPU for inference, cheaper
    endpoint_name='my-model-prod'
)

# Make predictions
import json
result = predictor.predict({'inputs': [[5.1, 3.5, 1.4, 0.2]]})
print(result)

# Clean up (endpoints cost money even when idle!)
predictor.delete_endpoint()

AWS Bedrock — Managed LLM API

Bedrock gives you API access to foundation models (Claude, Llama, Titan) without managing any infrastructure. Pay per token, scale to zero automatically.

Python · Bedrock — Claude API

import boto3
import json

bedrock = boto3.client('bedrock-runtime', region_name='us-east-1')

response = bedrock.invoke_model(
    modelId='anthropic.claude-3-5-sonnet-20241022-v2:0',
    body=json.dumps({
        "anthropic_version": "bedrock-2023-05-31",
        "max_tokens": 512,
        "messages": [{"role": "user", "content": "Summarise this document..."}]
    })
)
result = json.loads(response['body'].read())
print(result['content'][0]['text'])

Cost Optimisation Tips

💡

Use Spot Instances

Save up to 90% on training jobs. SageMaker handles interruption and checkpointing automatically.

💡

Choose the right instance

ml.p3.2xlarge (~$3.82/hr) for training; ml.c5.xlarge (~$0.19/hr) for CPU inference.

💡

Delete idle endpoints

Endpoints bill 24/7. Use Lambda to auto-scale to zero or delete during off-hours.

💡

Use Serverless Inference

For intermittent traffic — only charges per prediction. Zero cost when idle.

Frequently Asked Questions

What IAM permissions does SageMaker need?

SageMaker needs an execution role with: AmazonSageMakerFullAccess (for SageMaker APIs), S3 read/write access for your data bucket, ECR access if using custom containers, and CloudWatch Logs access. Never use AdministratorAccess in production — follow least privilege.

How do I version my models in SageMaker?

Use the SageMaker Model Registry. Register models after training with estimator.register(). Models go through an approval workflow (Pending → Approved/Rejected) before being deployed to production endpoints. Model lineage tracks which training job and data version produced each model.

Frequently Asked Questions

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