Question 1

What's the difference between Amazon SageMaker and Amazon EC2 for machine learning?

Accepted Answer

EC2 gives you raw virtual machines—you install frameworks, manage scaling, and build deployment tooling yourself. SageMaker is a managed ML platform that adds purpose-built tools like managed training jobs, hyperparameter tuning, model registry, pipelines, and one-click/managed deployments, reducing the operational work needed to run ML end to end.

Question 2

When should I use Amazon SageMaker?

Accepted Answer

Use SageMaker when you want a managed way to build, train, and deploy ML models with less infrastructure work—especially if you need repeatable training pipelines, experiment tracking, scalable training on GPUs/CPUs, and production deployment patterns (real-time endpoints, batch inference). If you only need a quick experiment on a single machine or already have a mature custom ML platform, plain compute (like EC2/EKS) may be sufficient.

Question 3

How much does Amazon SageMaker cost?

Accepted Answer

SageMaker pricing is primarily pay-as-you-go and depends on what you run: notebook/Studio apps, training instances (type and duration), managed endpoints for real-time inference (instance type and uptime), batch transform jobs, data processing jobs, and optional features like model monitoring. Costs are driven by compute size (CPU/GPU), runtime hours, storage, and data transfer. To estimate, map each workflow step (training, tuning, hosting) to instance types and expected hours, then use the AWS Pricing Calculator.

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