Isbn: 9798258796349 - responsible by design: ai safety, alignment, and trust engineering for production machine learning systems (5 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: new. Paperback. Build AI systems that are safe, reliable, and worthy of user trustPowerful AI systems bring powerful risks.As machine learning moves into real world products, safety, alignment, and trust are no longer optional. They are core engineering requirements."Responsible by Design" is a practical guide to building AI systems that are safe, aligned with user intent, and reliable in production using Python and modern ML practices.This book focuses on how to design, evaluate, and deploy AI responsibly from day one.Why AI safety and trust matterUncontrolled AI systems can lead to: harmful or biased outputsunpredictable behaviorsecurity vulnerabilitiesloss of user trustregulatory and compliance risksResponsible engineering ensures systems behave as intended and remain trustworthy over time.What you will learnfundamentals of AI safety and alignmentidentifying and mitigating risks in ML systemsbias detection and fairness strategiesrobustness and reliability testinghandling adversarial inputs and prompt attacksdesigning safe interaction patternsevaluation and monitoring for trusthuman in the loop systemsgovernance, compliance, and auditabilitydeploying safe AI in production environmentsFrom model performance to system responsibilityThroughout the book, you will learn how to: design AI systems with safety in mindevaluate outputs beyond accuracy metricsimplement safeguards and controlsmonitor systems continuously in productionhandle failures and edge casesbuild trust with users and stakeholdersEach chapter focuses on real engineering decisions that impact safety.Practical applicationsAI powered SaaS platformsenterprise AI systemscustomer facing AI assistantsautomated decision systemscompliance driven applicationsThese examples reflect real world use cases where trust is critical.Who this book is forAI engineersmachine learning engineersdata scientistsproduct buildersbackend developers working with AIprofessionals deploying AI systemsIf you want to build AI systems that are not only powerful but also safe and trustworthy, this book provides the roadmap.Design responsibly.Align intelligently.Build trust into every system. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Build AI systems that are safe, reliable, and worthy of user trustPowerful AI systems bring powerful risks.As machine learning moves into real world products, safety, alignment, and trust are no longer optional. They are core engineering requirements."Responsible by Design" is a practical guide to building AI systems that are safe, aligned with user intent, and reliable in production using Python and modern ML practices.This book focuses on how to design, evaluate, and deploy AI responsibly from day one.Why AI safety and trust matterUncontrolled AI systems can lead to: harmful or biased outputsunpredictable behaviorsecurity vulnerabilitiesloss of user trustregulatory and compliance risksResponsible engineering ensures systems behave as intended and remain trustworthy over time.What you will learnfundamentals of AI safety and alignmentidentifying and mitigating risks in ML systemsbias detection and fairness strategiesrobustness and reliability testinghandling adversarial inputs and prompt attacksdesigning safe interaction patternsevaluation and monitoring for trusthuman in the loop systemsgovernance, compliance, and auditabilitydeploying safe AI in production environmentsFrom model performance to system responsibilityThroughout the book, you will learn how to: design AI systems with safety in mindevaluate outputs beyond accuracy metricsimplement safeguards and controlsmonitor systems continuously in productionhandle failures and edge casesbuild trust with users and stakeholdersEach chapter focuses on real engineering decisions that impact safety.Practical applicationsAI powered SaaS platformsenterprise AI systemscustomer facing AI assistantsautomated decision systemscompliance driven applicationsThese examples reflect real world use cases where trust is critical.Who this book is forAI engineersmachine learning engineersdata scientistsproduct buildersbackend developers working with AIprofessionals deploying AI systemsIf you want to build AI systems that are not only powerful but also safe and trustworthy, this book provides the roadmap.Design responsibly.Align intelligently.Build trust into every system. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…