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020 _a9783031318054
_9978-3-031-31805-4
024 7 _a10.1007/978-3-031-31805-4
_2doi
050 4 _aQ325.5-.7
072 7 _aUYQM
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aUYQM
_2thema
082 0 4 _a006.31
_223
100 1 _aLazzeri, Francesca.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMachine Learning Governance for Managers
_h[electronic resource] /
_cby Francesca Lazzeri, Alexei Robsky.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2024.
300 _aXIX, 108 p. 17 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aMachine learning.
650 0 _aEngineering
_xData processing.
650 0 _aArtificial intelligence
_xData processing.
650 0 _aBusiness
_xData processing.
650 0 _aMathematical statistics
_xData processing.
650 1 4 _aMachine Learning.
650 2 4 _aData Engineering.
650 2 4 _aData Science.
650 2 4 _aBusiness Analytics.
650 2 4 _aStatistics and Computing.
700 1 _aRobsky, Alexei.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031318047
776 0 8 _iPrinted edition:
_z9783031318061
856 4 0 _uhttps://doi.org/10.1007/978-3-031-31805-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c186727
_d186727