Tutorials Guide
๐ Sign Up for Our Hosted Tutorials
Experience hands-on learning with our Hosted Tutorials. Gain access to our hosted environment and explore FeatureByte in an interactive setting.
Learn to Use the FeatureByte User Interface¶
What Youโll Learn¶
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Catalog Creation: Organize and manage your data sources in a structured catalog.
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Data Modeling: Define tables and entities to support efficient feature computation.
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Use Case Structuring: Design and manage feature engineering workflows around business use cases.
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Ideation: Discover how FeatureByte automatically suggests relevant features and models based on your data and objectives.
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Experimentation: Build and iterate on feature lists, view models' key metrics, and select the best model for deployment.
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Deployment: Operationalize your final feature list and model seamlessly.
๐ Dataset Overview¶
The Credit Default Dataset includes seven tables:
| Table Name | Description |
|---|---|
| NEW_APPLICATION | Information about new loan applications. |
| CLIENT_PROFILE | Client demographic and profile information. |
| BUREAU | Records of previous credits taken by clients from other institutions. |
| PREVIOUS_APPLICATION | Details of prior loan applications by each client. |
| INSTALLMENTS_PAYMENTS | Logs of monthly installment payments for loans. |
| LOAN_STATUS | Current loan status over time. |
| CREDIT_CARD_MONTHLY_BALANCE | Monthly balance summaries for clientsโ previous credit cards. |
๐น Start Here¶
Learn to Use the FeatureByte SDK¶
Step 1: Set Up the SDK¶
Follow the SDK Tutorial Installation Guide for step-by-step setup instructions.
Step 2: Build an End-to-End Workflow¶
Using the same dataset as in the UI tutorials, learn to:
- Create a catalog and register your data model
- Define use cases and craft features
- Compute training data and train models
- Deploy and manage production-ready features
๐น Start Here¶
Step 3: Work with Item Data in the SDK¶
๐ Dataset Overview¶
The Grocery Dataset includes four tables:
| Table Name | Description |
|---|---|
| GroceryCustomer | Customer details such as name, address, and date of birth. |
| GroceryInvoice | Invoice details including timestamps and total amounts. |
| InvoiceItems | Items in each invoice, with quantities, discounts, and costs. |
| GroceryProduct | Product group and description for grocery items. |
๐น Start Here¶
Tutorial Differences
Both datasets include Event Tables, Slowly Changing Dimension Tables, and Dimension Tables.
| Dataset | Additional Table Type |
|---|---|
| Grocery Dataset | Item Table |
| Credit Default Dataset | Time Series Table |
Learn to Implement Custom Transformers¶
Enhance your text processing and feature engineering capabilities by integrating custom transformer models. Explore our Bring Your Own Transformer guide to learn how.