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Titlebook: Machine Learning Governance for Managers; Francesca Lazzeri,Alexei Robsky Book 2024 The Editor(s) (if applicable) and The Author(s), under

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發(fā)表于 2025-3-21 16:29:00 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Machine Learning Governance for Managers
編輯Francesca Lazzeri,Alexei Robsky
視頻videohttp://file.papertrans.cn/621/620400/620400.mp4
概述Helps data science managers to scale and become more data- and AI-driven.Helps break through the complexity and challenges of moving data science and machine learning projects to production.Helps orga
圖書封面Titlebook: Machine Learning Governance for Managers;  Francesca Lazzeri,Alexei Robsky Book 2024 The Editor(s) (if applicable) and The Author(s), under
描述.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 achiev
出版日期Book 2024
關(guān)鍵詞Machine Learning Governance; MLOps; Machine Learning Operations; Data Science Function and Management; D
版次1
doihttps://doi.org/10.1007/978-3-031-31805-4
isbn_softcover978-3-031-31804-7
isbn_ebook978-3-031-31805-4
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 20:51:08 | 只看該作者
Francesca Lazzeri,Alexei RobskyHelps data science managers to scale and become more data- and AI-driven.Helps break through the complexity and challenges of moving data science and machine learning projects to production.Helps orga
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發(fā)表于 2025-3-22 00:53:03 | 只看該作者
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發(fā)表于 2025-3-22 11:25:52 | 只看該作者
Understanding Business Goals, a solid strategy to achieve those goals. However, it is very easy to drown in the weeds of metrics and goals and target everything and nothing all at once. Now, this is not to discourage and accuse leaders from establishing wrong goals, but more to provide a different view to measuring what is right.
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,Unifying Organizations’ Machine Learning Vision, operations of data science and machine learning becomes increasingly important. However, scaling data processing infrastructure is not as simple as adding more resources, and there are many challenges that arise when working with increased demand for insights and large datasets that constantly grow.
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