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Machine learning governance for managers

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning governance for managers/ by Francesca Lazzeri, Alexei Robsky.
Author:	Lazzeri, Francesca.
other author:	Robsky, Alexei.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	xix, 108 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Understanding Business Goals -- 2. Measure the Right Things -- 3. Searching for the Right Tools -- 4. MLOps Governance & Architecting the Data Science Solution -- 5. Unifying Organizations' Machine Learning Vision.
Contained By:	Springer Nature eBook
Subject:	Machine Learning. -
Online resource:	https://doi.org/10.1007/978-3-031-31805-4
ISBN:	9783031318054

Machine learning governance for managers
Lazzeri, Francesca.

Machine learning governance for managers[electronic resource] /by Francesca Lazzeri, Alexei Robsky. - Cham :Springer International Publishing :2024. - xix, 108 p. :ill., digital ;24 cm.

1. Understanding Business Goals -- 2. Measure the Right Things -- 3. Searching for the Right Tools -- 4. MLOps Governance & Architecting the Data Science Solution -- 5. Unifying Organizations' Machine Learning Vision.

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.

ISBN: 9783031318054

Standard No.: 10.1007/978-3-031-31805-4doiSubjects--Topical Terms:

3382522
Machine Learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning governance for managers
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245 1 0 $a Machine learning governance for managers $h [electronic resource] / $c by Francesca Lazzeri, Alexei Robsky.
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505 0 $a 1. Understanding Business Goals -- 2. Measure the Right Things -- 3. Searching for the Right Tools -- 4. MLOps Governance & Architecting the Data Science Solution -- 5. Unifying Organizations' Machine Learning Vision.
520 $a 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.
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