Working with AI Data

A practical, ethical, and strategic guide to understanding, preparing, sourcing, annotating, and sustaining data for artificial intelligence.
  • ISBN-13: 979-8243189545
  • Publication date: Jan. 8, 2026 Print length: 460 pages Publisher: Montbleau.ca Academic Press

Build AI on better data

Working with AI Data takes readers from the foundations of data literacy to the practical realities of building reliable, responsible AI systems. It explains how raw information becomes insight, how bias enters datasets, why data cleaning matters, and how modern data pipelines support sustainable AI in production.
"True intelligence begins with data literacy: the ability to critically assess, ethically handle, and creatively transform raw information into actionable insight."

Inside the book

  • Structured across ten chapters, the book connects foundational concepts with hands-on practitioner frameworks.

  • Data: the atomic unit of intelligence
  • Data types: tabular, text, image, audio, video, and time series
  • Data science: statistics, visualization, and exploratory analysis
  • Bias in data: detection, mitigation, and accountability
  • Data sanitization: cleaning and feature engineering
  • Databases and big data: SQL, NoSQL, Spark, lakes, and pipelines
  • Data sourcing: quality, provenance, consent, and synthetic data
  • Data annotation: human-in-the-loop workflows and AI tooling
  • Data sustainability: deployment, drift, monitoring, and MLOps
  • Data scoping: turning AI ideas into realistic projects

Who should read it?

Students & Beginners
Learn the vocabulary, principles, and mindset needed to understand AI data from the ground up.
Data Scientists & ML Engineers
Use the book as a reference for bias, feature engineering, data pipelines, MLOps, and production monitoring.
Managers & Product Owners
Build the literacy needed to scope AI projects, evaluate data readiness, and advocate for responsible practice.

Core themes

Data literacy
Understand the journey from raw data to information, knowledge, and responsible decision-making.
Ethics in practice
Treat fairness, bias, transparency, privacy, and consent as core dimensions of data quality.
Production readiness
Move beyond notebooks into monitoring, drift detection, retraining, governance, and long-term sustainability.

About the author

Pierre Montbleau writes at the intersection of data, artificial intelligence, cybersecurity, and enterprise systems. Through Montbleau.ca Academic Press, he creates practical resources that help readers understand complex technology with clarity and responsibility.
  • Expert in data, AI, cybersecurity, and enterprise systems.
  • Translates complex technology into practical knowledge.
  • Focuses on real-world applications and solutions.
  • Promotes responsible and ethical innovation.
  • Empowers professionals to build future-ready skills.
(514) 949-7697
pierre@montbleau.ca
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