All 7 Amazon SageMaker & Machine Learning on AWS questions
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What is Amazon SageMaker and what stages of the machine learning workflow does it support? What is SageMaker hyperparameter tuning, and how does it help improve model accuracy? How do SageMaker endpoints support real time inference, and how do they differ from batch transform? What is SageMaker Studio, and how does it improve the machine learning development experience? How does SageMaker support MLOps practices such as model monitoring and automated retraining pipelines? How does SageMaker support multi model endpoints and model versioning for cost efficient deployment at scale?
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How does SageMaker handle model training, and what compute resources does it use?

Beginner

Answer

SageMaker training jobs let you specify a training script or use a built in algorithm, point to your training data typically stored in S3, and choose the type and number of compute instances needed, and SageMaker automatically provisions those instances, runs the training job, saves the resulting model artifacts back to S3, and then automatically terminates the instances once training completes, meaning you only pay for the exact time spent training.
aws sagemaker create-training-job --training-job-name my-training-job --algorithm-specification TrainingImage=xgboost,TrainingInputMode=File --resource-config InstanceType=ml.m5.xlarge,InstanceCount=1,VolumeSizeInGB=10
Real-world example A retail company trains a demand forecasting model using SageMaker, specifying that four large GPU instances should be used only for the two hours needed to complete training, avoiding the cost of keeping expensive hardware running continuously.

Common follow-ups: How does SageMaker pricing work for training jobs compared to real time inference?;Can SageMaker training jobs use spot instances to reduce cost?

EC2 & Compute;AWS Cost Management & Billing
All 7 Amazon SageMaker & Machine Learning on AWS questions