The Model Lifecycle: From Data to Deployment: How Machine Learning Systems Are Built, Tested, and Kept Honest - Couverture souple

SOCIETY, DIGITAL

Couverture souple

ISBN 13 : 9798163681600

Editeur : Independently published, 2026

Synopsis

The Model Lifecycle: From Data to Deployment — How Machine Learning Systems Are Built, Tested, and Kept Honest

Building a machine learning model is only the beginning. The real challenge is ensuring that it works reliably, remains useful as conditions change, and produces results that can be trusted.

The Model Lifecycle explores the complete journey of machine learning systems, from raw data and initial experimentation to deployment, monitoring, and continuous improvement. It examines the technical processes, organizational decisions, and governance practices that determine whether an AI model becomes a dependable business asset or a source of hidden risk.

Machine learning is often presented as a sequence of algorithms, training runs, and performance benchmarks. In practice, successful systems depend on much more: data quality, experimental design, validation, reproducibility, infrastructure, human oversight, and the ability to detect when a model stops behaving as expected.

This book examines the stages that connect data preparation, feature engineering, model selection, training, evaluation, deployment, and ongoing maintenance. It explains why performance in a controlled experiment does not always translate into reliable results in the real world, and why a model that works today may become inaccurate tomorrow.

Inside this book, discover:

• How data collection, preparation, and feature engineering shape model performance.

• The principles of training, validation, testing, and avoiding data leakage.

• How to evaluate models beyond headline accuracy and select meaningful metrics.

• Why reproducibility, version control, and experiment tracking matter.

• How deployment pipelines and machine learning operations (MLOps) support reliable systems.

• How monitoring can reveal data drift, model degradation, and unexpected behavior.

• Why explainability, fairness, documentation, and human oversight are essential to responsible AI.

• How feedback loops and retraining help models adapt to changing environments.

Beyond the technical workflow, The Model Lifecycle explores a fundamental question: what does it mean to keep an AI system honest?

Reliable machine learning requires more than achieving a high score on a benchmark. It requires transparent evaluation, clear accountability, continuous monitoring, and the willingness to revise or retire systems that no longer meet their intended objectives.

For data scientists, machine learning engineers, technology leaders, students, and business professionals, this book offers a structured perspective on the processes that make AI systems sustainable and trustworthy.

Whether you are developing your first predictive model or overseeing machine learning in a complex organization, understanding the entire lifecycle is essential to making better technical and strategic decisions.

A model is not finished when training ends. It earns trust through testing, deployment, monitoring, and continuous accountability.

Détails de l'édition

• Éditeur: Independently published
• Date d'édition: 2026
• Langue: anglais
• ISBN 13: 9798163681600
• Reliure: Broché
• Nombre de pages: 81
• Coordonnées du fabricant: Printed by Amazon on behalf of the author; https://www.amazon.fr/hz/contact-us; ; c/o Amazon Media EU S.à.r.l., 38 Avenue John F. Kennedy; Luxembourg; L-1855; Luxembourg