Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today's business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance.Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized.Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don't let challenges in machine learning hinder your organization's growth - unlock its potential with this essential guide.

Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today''s business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance.

Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized.

Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don''t let challenges in machine learning hinder your organization''s growth - unlock its potential with this essential guide.

This book gives practitioners a concrete, systematic framework for designing evals that make AI systems safe, robust, and customer-ready before they reach production. Drawing on real-world failures, from chatbots that went off the rails to shopping assistants that hallucinated product information, it shows how seemingly small evaluation gaps can cascade into legal, financial, and reputational crisis, and how to close those gaps with disciplined, systematic testing.Moving from foundational concepts to advanced practice, Reliable Evals for LLMs and AI Agents introduces the four core levers of effective evals: sets, templates, metrics, and evaluators. It then extends these to the unique challenges of autonomous AI agents, where systems perceive, reason, act, and adapt in iterative loops that demand fundamentally different eval approaches. Along the way, it guides readers through benchmark selection, custom eval set design, statistical rigor in metrics, human and LLM-as-a-judge rating strategies, and the infrastructure needed to automate evals at scale.For engineering leaders, applied researchers, data scientists, and product teams shipping LLM- and agent-powered experiences, this volume offers a blueprint for building eval flywheels that continuously improve AI quality. It shows how to progress from ad-hoc checks to production-grade eval systems, align model metrics with real user satisfaction, integrate offline evals with online A/B testing, and design accessible interfaces that democratize rigorous testing across an organization.

Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today's business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance.Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized.Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don't let challenges in machine learning hinder your organization's growth - unlock its potential with this essential guide.

Machine Learning Governance for Managers provides readers with the knowledge to unlock insights from data and leverage AI solutions. In today''s business landscape, most organizations face challenges in scaling and maintaining a sustainable machine learning model lifecycle. This book offers a comprehensive framework that covers business requirements, data generation and acquisition, modeling, model deployment, performance measurement, and management, providing a range of methodologies, technologies, and resources to assist data science managers in adopting data and AI-driven practices. Particular emphasis is given to ramping up a solution quickly, detailing skills and techniques to ensure the right things are measured and acted upon for reliable results and high performance.

Readers will learn sustainable tools for implementing machine learning with existing IT and privacy policies, including versioning all models, creating documentation, monitoring models and their results, and assessing their causal business impact. By overcoming these challenges, bottom-line gains from AI investments can be realized.

Organizations that implement all aspects of AI/ML model governance can achieve a high level of control and visibility over how models perform in production, leading to improved operational efficiency and a higher ROI on AI investments. Machine Learning Governance for Managers helps to effectively control model inputs and understand all the variables that may impact your results. Don''t let challenges in machine learning hinder your organization''s growth - unlock its potential with this essential guide.

This book gives practitioners a concrete, systematic framework for designing evals that make AI systems safe, robust, and customer-ready before they reach production. Drawing on real-world failures, from chatbots that went off the rails to shopping assistants that hallucinated product information, it shows how seemingly small evaluation gaps can cascade into legal, financial, and reputational crisis, and how to close those gaps with disciplined, systematic testing.Moving from foundational concepts to advanced practice, Reliable Evals for LLMs and AI Agents introduces the four core levers of effective evals: sets, templates, metrics, and evaluators. It then extends these to the unique challenges of autonomous AI agents, where systems perceive, reason, act, and adapt in iterative loops that demand fundamentally different eval approaches. Along the way, it guides readers through benchmark selection, custom eval set design, statistical rigor in metrics, human and LLM-as-a-judge rating strategies, and the infrastructure needed to automate evals at scale.For engineering leaders, applied researchers, data scientists, and product teams shipping LLM- and agent-powered experiences, this volume offers a blueprint for building eval flywheels that continuously improve AI quality. It shows how to progress from ad-hoc checks to production-grade eval systems, align model metrics with real user satisfaction, integrate offline evals with online A/B testing, and design accessible interfaces that democratize rigorous testing across an organization.