How-Tos
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Task-focused guides for the most common things you’ll do in Corridor, grouped by where they fall in the analytical lifecycle. Pick whichever topic matches what you’re trying to do.
| Topic | Description |
|---|---|
| Registration | |
| Register a Table | Bring source data into the platform by registering it as a DataTable. Tables are the foundation that data elements, features, and models read from. |
| Common Registration Info | The fields shared by every registered object: Name, Alias, Entity, and the Properties section (Description, Permissible Purpose, Group, Keywords). |
| Register a Data Element | Expose a single column from a registered table as a governed, reusable input. |
| Register a Data Aggregate | Compute a value across multiple rows (an aggregated Data Element) in Python, Pandas, or Spark. |
| Register a Feature | Build a derived variable on top of data elements or other features. Features are inputs to models and policies. |
| Register a Feature Aggregate | Roll up rows from a lower entity level into a single value at the feature's entity (an aggregated Feature). |
| Register a Model Definition | Import or upload a trained model so it can be governed, simulated, and used inside policies. |
| Register a Model Explainer | Attach explanation logic to a model: feature-importance contributions or reason codes per prediction. |
| Register a Policy Decision Workflow | Define decision logic that combines features and models to approve, decline, price, or trigger downstream actions. |
| Register Reports | Define reusable analytics and visualizations that run on every job, from confusion matrices to tracked metrics over time. |
| Register a Global Function | Register reusable Python that can be called by alias across aggregates, features, reports, and policies. |
| Analytics | |
| Run a Simulation | Execute a registered object on a dataset to validate performance and generate evidence for approval. |
| Understand Job Output | The columns a job's result table produces for each object type (id, inputs, output, transforms, dependent, block/segment/rule), and where you read them. |
| Compare Two Simulations | Put two simulation results side by side for champion-vs-challenger, version-vs-version, or sample-vs-sample analysis. |
| Do a Quick Test | Run an object's logic on one record interactively to sanity-check it while iterating. |
| Run a Single Record | Trace one application through every block, rule, and offer to debug a specific decision. |
| Run a What-If Analysis | Create a sandbox variant of a policy, change its rules, and simulate the impact without touching production. |
| Run a Portfolio Analysis | Run policies and features across a slice of your portfolio and assemble the results into a single dataset. |
| Use the Notebook (Corridor Python Package) | Query, declare, and run registered objects from code in the hosted Notebook using the corridor Python package. |
| Move to Production | |
| Approve an Object | Move a draft object through the approval workflow so it can be used outside your draft workspace. |
| What's in the Artifact | Understand the production-ready package the platform produces for an approved object, and what it bundles. |
| Monitoring | |
| Monitor your Model or Policy | Track performance, drift, and decision distributions on objects running in production. |
| Set up Alerts | Configure notifications for performance thresholds, drift, and operational events. |
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