Practical Generative AI: From Concept to Deployment: Building and Deploying Ethical AI-Powered Solutions

From concept to deployment 🤖

Generative AI has moved quickly from research labs into real products, but building reliable systems requires more than a clever prompt. Practical Generative AI: From Concept to Deployment offers a structured path through the technical and organisational decisions that turn an idea into a working, ethical AI solution. Written by Pramod Singh and James McKeone, this Apress title brings together data engineering, model selection, evaluation, deployment, and business strategy.

What the book covers

The book begins by explaining the rise of generative AI, how it differs from traditional machine learning, and why responsible use matters. From there, it works through the practical stack:

• Data handling, embeddings, vector databases, and PII management
• Prompt engineering techniques for modern models and agents
• Evaluation methods, metrics, and iterative improvement
• Open-source language models and fine-tuning approaches, including LoRA
• Cloud architecture, monitoring, and the stages from POC to MVP to production
• Business use cases across finance, insurance, healthcare, procurement, supply chain, and recruitment

Technical depth with a deployment focus ⚙️

Chapters move between conceptual explanation and hands-on implementation. Case studies cover evaluation metrics, early prompting and feedback loops, and the criteria that help teams decide whether to build with open-source models. The material addresses the operational realities of GenAI: managing data pipelines, handling sensitive information, selecting models, and measuring whether an application actually works.

Responsible AI as a design requirement 🧠

Ethical considerations are not treated as an afterthought. The authors discuss transparency, explainability, responsible AI frameworks, and the challenges organisations face when putting those principles into practice. For teams building customer-facing or regulated applications, this grounding can help connect technical choices with governance and trust requirements.

Who it is for

Data scientists, machine learning engineers, AI architects, and technical leaders will find relevant guidance here. The book is also useful for professionals who need to understand how generative AI projects move from proof of concept to production, including those working at the intersection of technology, strategy, and business operations.

About the authors

Pramod Singh is an Expert Associate Partner at Bain & Company, where he leads the Data Science & Machine Learning guild in the Asia Pacific region and heads Bain’s generative AI ringfence group in APAC. James McKeone is a data scientist specialising in generative AI development, with experience defining architectures for end-to-end data science products and leading teams.

A practical companion for the journey from idea to production ☁️

If you want a clear view of how generative AI systems are actually assembled, evaluated, and deployed—and how ethical considerations fit into that work—this book provides a grounded, up-to-date map of the field.