Objectives to cover:

• Introduction to Data-Centric AI – Exploring the growing importance of high-quality data in modern AI development.

• From Model-Centric to Data-Centric – Understanding the shift in focus from algorithmic improvements to data refinement.

• Significance of Data Quality – Highlighting how clean, diverse, and representative data drives better AI outcomes.

• Metrics for Data Quality – Identifying key indicators such as accuracy, completeness, consistency, and relevance.

• Real-World Case Studies – Demonstrating how data quality directly impacts model performance across industries.

• Improvement Techniques – Reviewing methods like data augmentation, labeling optimization, and noise reduction.

• Implementation Challenges – Addressing common hurdles such as resource constraints and tooling limitations.

• Applications and Tools – Showcasing practical tools, frameworks, and best practices for data-centric AI adoption.

• Conclusion and Future Directions – Summarizing insights and outlining the path forward for data-first AI strategies.

Reference:  APA style