SageMaker is AWS’s fully managed platform for building, training, and deploying machine-learning models. It covers the whole ML lifecycle so data scientists don’t have to wrangle infrastructure.
What the exam expects
Know SageMaker is the go-to for custom ML: preparing data, training models, and hosting them for inference. It’s different from the pre-built AI services (Rekognition for images, Comprehend for text) — those are ready-made APIs, while SageMaker is for building your own models.
Test yourself
A data science team wants to build, train, and deploy their own custom machine-learning model without managing training infrastructure. Which service?
- Amazon Rekognition
- Amazon SageMaker
- Amazon Comprehend
- AWS Glue
👉 Click to reveal the answer & explanation
Correct answer: B. SageMaker is the managed platform for building, training, and deploying custom ML models. Rekognition (A) and Comprehend (C) are pre-built AI APIs, not for custom model building; Glue (D) is ETL.
Related topics
Amazon Comprehend · Amazon Rekognition · AWS Glue
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