Part 10/13:
AI-Driven Data Governance: Embedding AI to identify sensitive data, enforce policies, and ensure regulatory compliance across diverse data sources.
AI-Augmented Data Engineering Teams: Transitioning from manual, rule-based processes to AI-enhanced workflows, enabling teams to focus on strategic insights rather than plumbing work.
Unified Observability and Explainability: Building platforms that provide end-to-end visibility into data pipelines, with AI delivering insights and justifications for pipeline performance or failures.
Synthetic Data for Model Development: Using AI to generate diverse datasets, overcoming data scarcity, and enhancing ML model robustness, without compromising privacy.