AI in Practice: Applied Learning across Industries

AI delivers value when it meets real constraints. AI in Practice examines how learning systems are applied across industries—where context, regulation, and impact shape design.
Through sector-spanning examples, the book shows how objectives differ in healthcare, finance, manufacturing, retail, and public services. Readers learn how teams adapt models to domain realities, balance accuracy with explainability, and integrate AI into existing operations.
The emphasis remains on execution. Success depends on framing the right questions, aligning stakeholders, and iterating responsibly. Trade-offs—speed versus safety, scale versus control—are addressed as practical decisions rather than theoretical debates.
Key application insights include:
• Adapting ML to domain constraints
• Operationalizing models within workflows
• Measuring impact beyond accuracy
• Managing risk, compliance, and trust
• Learning from deployment feedback
AI in Practice grounds machine learning in reality, demonstrating how applied learning succeeds when systems are designed for people, processes, and purpose—not just performance.