AI World 4 · ML Pipeline · Stage 3

Train and Tune

No code for the classifier, the team's script unchanged, a 6-hour job at up to 90% less, a tuning budget spent wisely, last month's search reused, and runs you can compare.

Built-in algorithms and script mode
Managed spot training
Automatic model tuning
MLflow

Domain: AI and machine learning
World: AI World 4: ML Pipeline (Associate)
Game type: Solution board: Drop services into each row's slots, then run records through them.
Time: About 25 minutes on a first attempt (estimated)
Exam guides: MLA-C02Skill 2.1.6: Assess tradeoffs between ML model performance, training time, and cost; Skill 2.2.1: Apply Amazon SageMaker AI built-in algorithms and common ML libraries; Skill 2.2.2: Configure SageMaker AI script mode with supported frameworks for simplicity and performance; Skill 2.2.3: Implement hyperparameter optimization (for example, SageMaker AI automatic model tuning [AMT]); Skill 2.2.4: Implement training time reduction techniques (for example, early stopping, distributed training); Skill 2.2.7: Adjust fundamental hyperparameters (for example, epoch, steps, batch size); Skill 2.3.1: Perform reproducible experiments (for example, by using MLflow on SageMaker AI, Amazon Bedrock evaluations, Amazon Bedrock Prompt Management)