{"version":"1.0","type":"rich","title":"xgb-workflow.sh","description":"This example demonstrates an end-to-end machine-learning workflow that processes transaction data, runs a forecast model, and uploads the results to Amazon S3. It shows the orchestration of distinct stages—ETL, forecasting, and cloud storage—within a single bash script. The workflow invokes Apache Spark via `spark-submit` for the `etl_transaction.py` ETL job, uses `Rscript` to execute `forecast.R` for prediction, and relies on the AWS CLI (`aws s3 cp`) for uploading output directories. The script also handles parameter validation and logs all activity to `xgb-workflow.log`.","provider_name":"VizHub AI Studio","provider_url":"https://next.vizhub.com","html":"<iframe src=\"https://next.vizhub.com/viz/gist-2fe1c3cb2a25e1a8a62400bbfb211bc2?mode=embed\" width=\"960\" height=\"500\" scrolling=\"no\" frameborder=\"1\"></iframe>","width":960,"height":500,"cache_age":3600}