03721nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020003700100024003500137050001400172072001700186072002300203072001600226100008800242245010800330250001800438264007500456300005400531336002600585337002600611338003600637347002400673505022400697520163000921650002902551650003402580650004602614650003802660650005302698650002902751650002902780650001802809650003102827650003702858700008402895710003402979773002603013776003603039776003603075856004603111912001403157912001503171999001703186978-3-031-31805-4DE-He21320251030110837.0cr nn 008mamaa231124s2024    sz |    s    |||| 0|eng d  a97830313180549978-3-031-31805-47 a10.1007/978-3-031-31805-42doi 4aQ325.5-.7 7aUYQM2bicssc 7aMAT0290002bisacsh 7aUYQM2thema1 aLazzeri, Francesca.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut96746210aMachine Learning Governance for Managersh[electronic resource] /cby Francesca Lazzeri, Alexei Robsky.  a1st ed. 2024. 1aCham :bSpringer International Publishing :bImprint: Springer,c2024.  aXIX, 108 p. 17 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda0 a1. 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.  aMachine 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. 0aMachine learning.955059 0aEngineeringxData processing. 0aArtificial intelligencexData processing. 0aBusinessxData processing.959544 0aMathematical statisticsxData processing.96475014aMachine Learning.95505924aData Engineering.96605924aData Science.24aBusiness Analytics.96618624aStatistics and Computing.9664461 aRobsky, Alexei.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut9674632 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303131804708iPrinted edition:z978303131806140uhttps://doi.org/10.1007/978-3-031-31805-4  aZDB-2-SCS  aZDB-2-SXCS  c56609d56609