Quick Start Guide
Step 1: Review the Workflow¶
Familiarize yourself with the FeatureByte workflow, from creating features to serving them and managing their lifecycle.
Step 2: Learn Through Tutorials¶
Explore FeatureByteβs capabilities with our interactive tutorial series. These tutorials will guide you through key concepts such as catalog creation, data modeling, and use case structuring. You'll also dive into Feature Ideation, which automatically suggests relevant features, and learn how to build feature lists, compute training data, and deploy features.
π Hands-on Learning with Hosted Tutorials¶
For a hands-on learning experience, sign up for our Hosted Tutorials to acccess our hosted environment.
π Available Tutorials¶
1. Loan Applications Dataset Tutorials¶
π Dataset Overview¶
The Loan Applications Dataset consists of seven tables:
- NEW_APPLICATION: Contains information about new loan applications submitted by clients.
- CLIENT_PROFILE: Describes the profile for each client.
- BUREAU: Lists all previous credits taken by clients from other financial institutions, as reported to the credit bureau.
- PREVIOUS_APPLICATION: Details prior loan applications made by the client.
- INSTALLMENTS_PAYMENTS: Logs monthly installments for loans at the time of payment.
- LOAN_STATUS: Captures the current status of each loan over time.
- CREDIT_CARD_MONTHLY_BALANCE: Provides monthly balance summaries for credit cards previously held by the client with the institution.
πΉ Start Here:¶
2. Grocery Dataset Tutorial¶
Walk through an end-to-end workflow using real-world data from a grocery store chain.
π Dataset Overview¶
The Grocery Dataset consists of four tables:
- GroceryCustomer β Contains customer details, including name, address, and date of birth.
- GroceryInvoice β Stores invoice details, including timestamp and total amounts.
- InvoiceItems β Lists items in each invoice, including quantity, total cost, applied discounts, and product IDs.
- GroceryProduct β Provides product group descriptions for grocery items.
πΉ Start Here:¶
Tutorial Differences
Both datasets include Event Tables, Slowly Changing Dimension Tables, and Dimension Tables.
Dataset | Additional Table Type |
---|---|
Grocery Dataset | Item Table |
Loan Applications Dataset | Time Series Table |
UI tutorials provide a comprehensive hands-on experience with Feature Ideation and the complete UI workflow, while SDK tutorials focus on advanced feature customization using FeatureByte's declarative framework in Python.
For a deeper understanding how to manage changes in source table availability and quality, check out the UI Grocery Tutorials.
Step 3: Install FeatureByte SDK¶
Refer to the Installation Guide for detailed instructions tailored to different operational environments.
Step 4: Connect to Your Data Warehouse¶
Ensure seamless integration with your existing data infrastructure by following our guides to connect with DataBricks, Snowflake, BigQuery and Spark.
Verify that you have Read Access for source tables and Write Access for the feature store.
Step 5: Implement Custom Transformers¶
Enhance your text processing and feature engineering capabilities by integrating custom transformer models. Visit our 'Bring Your Own Transformer' to learn how.
Step 6: Scale Your Enterprise AI Efforts with FeatureByte Enterprise¶
Contact us today for a demonstration or to learn more about how we can help you transform your AI aspirations into reality.