Why C is the answer

SageMaker automatic model tuning

SageMaker automatic model tuning runs training jobs across your ranges to find the best hyperparameters.

In detail

• Automatic model tuning searches hyperparameters.
• Limit the budget with a maximum number of jobs.

See it in different ways

mindmap
  root((SageMaker AI))
    Data
      Data Wrangler
      Ground Truth
      Clarify
    Train
      JumpStart
      Automatic model tuning
      Debugger
    Deploy
      Pipelines
      Model Registry
    Operate
      Model Monitor

flowchart LR
  S0["Set ranges"] --> S1["Run training jobs"] --> S2["Compare metric"] --> S3["Best hyperparameters"]

Why the other options are wrong

A. SageMaker Debugger

Debugger inspects training.

B. SageMaker Autopilot data exploration

Data exploration does not tune hyperparameters.

D. SageMaker Experiments

Experiments track runs.

Related questions

• Question 28You want your own TensorFlow code with SageMaker AI managing the infrastructure. What do you use?
• Question 3A model works well in training but fails on validation data. Which change most directly reduces overfitting?
• Question 22A training job keeps running after validation loss stops improving. What saves time?
• Question 27You need a pretrained model, with quick deployment on SageMaker AI. Where do you look?

Learn more

• Our lesson: Model development
• Bedrock evaluations