SageMaker Jobs: Processing, Training, Inference & HPT
Overview Before building automated pipelines, it helps to understand SageMaker’s individual building blocks. This project exercises all four core SageMaker job types using a Walmart retail sales dataset and SageMaker’s built-in XGBoost algorithm. Each job type solves a distinct phase of the ML workflow and runs on managed, ephemeral compute — no servers to provision or maintain. Job Type Purpose Processing Job Data prep, feature engineering, evaluation Training Job Model fitting Batch Transform Job Offline inference on large datasets Hyperparameter Tuning Job Automated hyperparameter search The Dataset Three CSV tables from Walmart historical sales data — features, weekly sales, and store metadata — merged and engineered into a regression dataset predicting weekly store sales. ...