Step 1
Open the workspace
Start in Forecasting and confirm the active project-backed workflow shell.
Operational AI Quickstart
Forecasting visual quickstart
Select a project, stage a forecasting dataset, train or activate a saved model version, then run and review forecast outputs.
Visual walkthrough
Follow the workflow step by step, using the screenshots where available and the concise action notes throughout.
Before you start
Select or create the right Forecasting project first.
Know whether you want a univariate or multivariate starter path.
Step 2
Stage the dataset
Upload a forecasting CSV or load a validated starter sample inside the selected project.
Step 3
Train or activate a version
Use Configure to review lifecycle state, train a saved version, and choose the active model path.
Step 4
Run the forecast
Execute the forecast against the active model or a selected saved version.
Step 5
Review forecast outputs
Inspect returned forecast values, confidence ranges, and result interpretation surfaces.
Expected outputs
Forecast values and execution context
Confidence bounds or interval outputs when available
Saved-model version context and result explanation surfaces
Common mistakes
Skipping project selection before staging data
Trying to run before a saved model version exists for the project
Using the wrong dataset shape for the intended forecast path
Next steps
Move from walkthrough to the right working page
Open the live workspace when you are ready to execute, or use the related product, use case, and spec pages when you need more context first.
Navigate
Keep this quickstart connected to the resource library
Return to the quickstart index for another product walkthrough, or go back to the resource hub for use cases, input specs, and how-to guides.