03917nam a22005655i 4500 978-3-031-31805-4 DE-He213 20251030110837.0 cr nn 008mamaa 231124s2024 sz | s |||| 0|eng d 9783031318054 978-3-031-31805-4 10.1007/978-3-031-31805-4 doi Q325.5-.7 UYQM bicssc MAT029000 bisacsh UYQM thema Lazzeri, Francesca. author. aut http://id.loc.gov/vocabulary/relators/aut 67462 Machine Learning Governance for Managers [electronic resource] / by Francesca Lazzeri, Alexei Robsky. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Springer, 2024. XIX, 108 p. 17 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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, monitoringmodels 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. 55059 Engineering Data processing. Artificial intelligence Data processing. Business Data processing. 59544 Mathematical statistics Data processing. 64750 Machine Learning. 55059 Data Engineering. 66059 Data Science. Business Analytics. 66186 Statistics and Computing. 66446 Robsky, Alexei. author. aut http://id.loc.gov/vocabulary/relators/aut 67463 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031318047 Printed edition: 9783031318061 https://doi.org/10.1007/978-3-031-31805-4 ZDB-2-SCS ZDB-2-SXCS 56609 56609