Machine Learning Governance for Managers

ნოე. 2023 · Springer Nature

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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 comp...

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Francesca Lazzeri, Ph.D. is an experienced data and machine learning scientist with over fifteen years of academic research, tech industry and engineering team building/management experience. Francesca is Professor of machine learning at Columbia University and Principal Data Scientist Manager at Microsoft, where she leads an organization of data scientists and machine learning engineers building data science and machine learning applications. Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit.

Alexei Robsky possesses an impressive professional background spanning over twelve years, characterized by his proficiency in constructing technological products, guiding engineering and data science teams, and spearheading business growth through the application of data science solutions. Currently, Alexei is a Data Science manager at Google, where he leads the SMB Growth Product Data Science team for Google Workspace. Previously, he contributed his expertise at Twitter, supporting a data science organization dedicated to optimizing Personalization and User Experience. Prior to Twitter, Alexei held the position of Principal Data Science Manager at Microsoft, where he successfully directed teams of data scientists, machine learning engineers, and data engineers in implementing cutting-edge solutions to enhance the customer experience on Microsoft Azure. Alexei's educational background includes an MBA from Duke University and a BSc in Electrical Engineering and Computer Science from Tel Aviv University.

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