000			03173nam a22003735i 4500
001			397747
003			ES-MaUEC
005			20240104162611.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			231124s2024 sz | s |||| 0|eng d
020			_a9783031318054
024	7		_a10.1007/978-3-031-31805-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
050	0	4	_aQ325.5-.7 _b2024
100	1		_aLazzeri, Francesca. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	0	0	_aMachine Learning Governance for Managers _cby Francesca Lazzeri, Alexei Robsky
250			_afirst edition 2024
264		1	_aCham _c2024 _bSpringer International Publishing
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF
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, 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.
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-31805-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
942			_2lcc _cLE
988			_aSpringer_Computer_2024
999			_c397747 _d397747