Reading List
Further reading galore.
Cited Sources
Data lake ↗
Wikipedia
Wikipedia's entry on data lakes — repositories of data stored in its natural, unprocessed format. Covers the history of the concept, comparison with data warehouses, and challenges such as data swamps where data lacks metadata or organization.
What is Artificial Intelligence? (MTU) ↗
Michigan Technological University
A comprehensive primer on the fundamentals of Artificial Intelligence, its history, and its various subfields.
AI Transparency (IBM) ↗
IBM Think
An overview of AI transparency, focusing on the need for explainability and openness in AI systems to build trust.
Knowledge Graph ↗
Wikipedia
Wikipedia's overview of knowledge graphs — covering their origins in structured data representation, their use by major technology companies (Google, Amazon, Meta), and how they differ from simple databases by encoding semantic relationships between entities.
Ontology (information science) ↗
Wikipedia
Wikipedia's entry on ontology as used in information science — defining it as a formal naming and definition of types, properties, and relationships among entities in a domain. Covers its role in the Semantic Web, knowledge representation, and how it differs from a taxonomy or thesaurus.
What is Responsible AI? (Syracuse) ↗
Syracuse University iSchool
A primer on responsible AI, detailing the ethical frameworks and practices required to develop AI safely and fairly.
Machine Learning Explained ↗
Sara Brown, MIT Sloan
Accessible overview of ML fundamentals from MIT Sloan — what ML is, how it differs from traditional programming, the three learning types (supervised, unsupervised, reinforcement), and its key limitations around explainability and bias.
What Is Machine Learning Observability? A Complete Guide ↗
Monte Carlo Data
Comprehensive guide to ML observability from Monte Carlo — covering how it differs from traditional monitoring, the five pillars of data health (freshness, distribution, volume, schema, lineage), and why visibility into model behavior in production is essential for catching drift and root-causing failures.
What Is KM? Knowledge Management Explained ↗
Michael E.D. Koenig, Ph.D., KMWorld
Deep-dive primer on Knowledge Management from KMWorld — its origins in management consulting, Davenport's classic definition, and the three-stage evolution from technology-first (best practices) to human factors (communities of practice) to findability (taxonomy and enterprise search).
What Is Technical Writing? ↗
Anna Fitzgerald
Accessible primer on what technical writing is and what technical writers do — translating complex technical information into clear, usable documentation for a specific audience. Covers common deliverables and the core skill set.