How digitalinnovationone/lab-aws-sagemaker-canvas-estoque works
This repository is a learning lab for inventory forecasting, not an application: it provides three CSV datasets and documents a user-run workflow in Amazon SageMaker Canvas. The graph preserves the documented selection, import, training, analysis, and prediction stages. These stages are Canvas activities, not repository modules; no source excerpts were provided, so relationships are supported only by the README.
Main entry points: dataset-1000-com-preco-promocional-e-renovacao-estoque.csv Promo stock data · dataset-1000-com-preco-variavel-e-renovacao-estoque.csv Variable-price data · dataset-500-curso-sagemaker-canvas-dio.csv Course data
Components 10
Datasets
- Promo stock data
datasets/dataset-1000-com-preco-promocional-e-renovacao-estoque.csv - Variable-price data
datasets/dataset-1000-com-preco-variavel-e-renovacao-estoque.csv - Course data
datasets/dataset-500-curso-sagemaker-canvas-dio.csv
Canvas workflow
- SageMaker Canvas
- Import dataset
- Train model
- Analyze performance
- Predict inventory
- Export results
Other components
- Learner
Connections 9
- Learner → Promo stock data: selects
README.md - Learner → Variable-price data: selects
README.md - Learner → Course data: selects
README.md - Learner → SageMaker Canvas: uploads dataset
README.md - SageMaker Canvas → Import dataset: imports
README.md - SageMaker Canvas → Train model: trains
README.md - SageMaker Canvas → Analyze performance: provides metrics
README.md - SageMaker Canvas → Predict inventory: generates forecasts
README.md - SageMaker Canvas → Export results: exports
README.md