The path runs from dataset to deployed component in six steps. Skipping the scorecard is how bad models reach production.
- Start a model from Analytics Studio
On the Analytics Studio home page click Create, then Model. Choose Create from Dataset and click Continue.
- Choose the dataset holding your history
Select the CRM Analytics dataset of past events where the result is already known, then click Next.
- Name the outcome and the direction
Under I Want to Predict, pick the outcome column. Set whether you want it maximized or minimized, then click Next.
- Let Einstein pick the columns
On Configure Model Columns leave the setting on Automated so the relevant fields get chosen for you, then click Create Model.
- Check the numbers before you ship
On Model Performance Overview, work through Assess Deployment Readiness and clear every data alert. Binary models get their threshold set on this screen too.
- Deploy it to an object
Click Deploy Model and pick the target object. Einstein maps most variables to fields itself; map the stragglers by hand, across relationships if needed. Choose whether to segment, mark actionable variables, then review and Deploy.
For a numeric outcome such as Amount or days to close. Judged on R-squared. The record shows a number in the column's own unit.
For a true or false outcome such as IsWon. Judged on AUC. The raw output is a probability until your threshold turns it into a verdict.
For a picklist-shaped outcome of three to ten distinct values, such as a tier or reason code. Bucket a wider column down in the recipe first.
- The model knows only the columns present when it trained. Adding a field later means building the model again.
- A near-perfect score should worry you, not please you. Salesforce's own training says a perfect model raises suspicion, so check Top Predictors before you believe it.
- Mapped fields that sit blank on a record degrade that record's prediction quietly, with no warning on the page.
