Data Enrichment with Code-Based Augmentation

This tutorial demonstrates how to enrich your data using code-based augmentation techniques in Corvic AI. Learn how to enhance your datasets with custom code transformations and augmentations.

What You’ll Learn

Creating custom data transformations using code-based augmentation
Implementing augmentation pipelines to enhance datasets
Best practices for data enrichment workflows

Key Concepts

Code-Based Augmentation

Code-based augmentation allows you to transform data using custom code, add computed fields and features, enrich datasets with external data, and create reusable augmentation logic for complex transformations.

Use Cases

Common use cases include feature engineering (creating new features from existing data), data cleaning (applying custom cleaning logic), data transformation (transforming formats and structures), external enrichment (combining data from multiple sources), and computed fields (generating derived fields using calculations).

Implementation Steps

Step 1: Define Your Augmentation Logic

Create custom code to define how you want to enrich your data:

def enrich_data(row):
    # Your custom augmentation logic
    row['enriched_field'] = compute_value(row)
    return row

Step 2: Apply Augmentation

Apply your augmentation code to your dataset by configuring augmentation settings, specifying input and output fields, and setting up transformation parameters.

Step 3: Validate Results

Review enriched data, validate transformations, check data quality, and monitor performance.

Best Practices

Write reusable, modular augmentation functions with proper error handling. Optimize code for large datasets and test augmentation logic on sample data first. Document your augmentation logic clearly for maintainability.

Data Sources

Learn about uploading and managing data sources.

Corvic Tables

Create Corvic Tables from your enriched data for distributed processing.

Spaces

Generate embedding spaces from enriched Corvic Tables.

Python Integration

Learn about Python integration for advanced workflows.

Video Tutorials

Data Enrichment with Code-Based Augmentation