AI-Powered Data Augmentation & Enrichment

Use GenAI to enhance existing datasets with new attributes or context.

Pillar

Data – Readiness, Governance, Quality & Ethics

Overview

This course introduces techniques for leveraging Generative AI to augment and enrich datasets, improving their value and usability for AI applications. Participants will learn how to generate synthetic data, add meaningful context, and enhance data diversity while maintaining quality and relevance.

Learning Objectives

Participants will be able to:

• Understand data augmentation concepts and their benefits

• Use GenAI tools to create synthetic data safely and effectively

• Enrich datasets with additional features and contextual information

• Evaluate the quality and impact of augmented data on AI models

• Implement augmentation strategies that preserve data integrity

Target Audience

• Data scientists and analysts

• AI developers and engineers

• Data engineers and architects

• Business leaders involved in data strategy

Duration

20 hours over 4 days (5 hours per day)

Delivery Format

• Hands-on workshops using GenAI augmentation tools

• Demonstrations of synthetic data generation techniques

• Group exercises to enrich datasets with context and features

• Discussions on best practices and data quality assessment

Materials Provided

• Sample datasets for augmentation practice

• Access to GenAI tools and platforms for data enrichment

• Guides on synthetic data generation and evaluation

• Reference materials on data augmentation strategies

Outcomes

• Practical skills in applying GenAI for data augmentation

• Ability to enhance dataset quality and diversity responsibly

• Understanding of augmentation impacts on model performance

• Frameworks for ongoing data enrichment in AI projects

Outline / Content

Day 1: Introduction to Data Augmentation and Enrichment

• Concepts and benefits of data augmentation

• Overview of GenAI capabilities for data enhancement

Day 2: Synthetic Data Generation Techniques

• Using GenAI to create realistic synthetic data

• Ensuring privacy and data safety in augmentation

Day 3: Enriching Data with Context and Features

• Adding new attributes and metadata using GenAI

• Improving dataset diversity and representativeness

Day 4: Evaluating and Implementing Augmented Data

• Assessing quality and impact on AI models

• Best practices for continuous data enrichment