Data for AI

Overview

This section emphasizes the critical role data plays in AI development and performance. It covers the processes involved in collecting, preparing, managing, and utilizing data to train and evaluate AI models effectively and responsibly.

Key Topics

• Data Collection: Methods for gathering data from various sources.
• Data Preprocessing: Cleaning and transforming raw data to make it suitable for AI models.
• Data Quality: Ensuring data accuracy, completeness, and reliability.
• Data Transformation and Normalization: Standardizing data formats and scales for consistency.
• Data Pipelines and Infrastructure:
  • ETL (Extract, Transform, Load): Processes for moving and preparing data for analysis.
  • Data Ingestion: Importing data into storage systems for analysis.
• Tokenization and Vectorization: Converting text and other data types into numerical representations for processing by AI models.
• Synthetic Data Generation: Creating artificial data to supplement real datasets.
• Data Privacy and Ethics:
  • Handling PHI (Protected Health Information): Managing sensitive health data responsibly.

By understanding these terms, readers can better navigate the foundational aspects of data preparation and its relevance to AI systems.