Mastering Data Provenance: Implementing Traceability in with Apache Airflow
Introduction
As Generative AI tools become a common name in the workplace, it’s crucial for teams working with these technologies to deepen their understanding of data — the fuel that powers these non-deterministic models. Learning more about good data generation and handling is not only essential for improving model accuracy but also for structuring it in a way that allows for standardized methodologies across various business units within an organization. In this blog, I aim to share key pointers that serve as a meaningful starting point on your journey to mastering data.
Generative AI tools are advanced software systems that use artificial intelligence to create new content. These tools are designed to generate various types of media, including text, images, music, and even code, by learning patterns and structures from existing data.
Data Ontology
Data ontology refers to a structured framework that defines the relationships between different data elements within a specific domain. It establishes a common vocabulary and a set of rules for the data, allowing different systems and stakeholders to understand and communicate about the data consistently. Ontologies provide a formal representation of knowledge, making it…
