In the age of artificial intelligence (AI), data is the foundation that powers solutions, including machine learning (ML) models, AI applications, and generative AI large language models (LLMs). However, the right quality data, in the right quantity, at the right time is often challenging to acquire.

This course explores the critical role of data in AI and generative AI, focusing on how to create, integrate, curate, manage, and augment data to enhance AI and generative AI model performance. Gokula Mishra will dive into the intricacies of data collection, data preparation, and the emerging field of synthetic data generation. Mishra will present synthetic data use cases and provide best practices for data specific to AI, machine learning, and generative AI.

You Will Learn

• The role of data in AI and generative AI
• Preparing and managing data for AI and generative AI
• Challenges with data for AI and generative AI applications
• Definition of synthetic data and its role in AI and machine learning
• Synthetic data use cases
• Generating and validating synthetic data
• Best practices for data in AI and generative AI application development

Geared To

• CDOs and CAOs
• Chief AI officers
• Data or AI directors and program leaders
• VP of data and analytics
• Analytics program leadership
• Data engineers
• Data scientists
• Data analytics team members
• Product managers
• Architects