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[null,"1.","Record Nr.",null,"UNINA990002580040403321"]
[null,null,"Autore",null,"ASYMPTOTIC AND COMPUTATIONAL ANALYSIS"]
[null,null,"Titolo",null,"Asymptotic and computational analysis : confewrence in honor of Frank W. J. Oliver's 65th birthday / Edited by R. Wong"]
[null,null,"Pubbl/distr/stampa",null,"New York : Marcel Dekker, 1990"]
[null,null,"ISBN",null,"0824783476"]
[null,null,"Descrizione fisica",null,"xii, 755 p. ; 25 cm"]
[null,null,"Collana",null,"Lecture notes in applied mathematics ; 124"]
[null,null,"Disciplina",null,"519.4"]
[null,null,"Locazione",null,"MAS"]
[null,null,"Collocazione",null,"MXXIX-A-375"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Papers presented at the International Symposium on Asymptotic Computational Analysis, held June 1989, Winnipeg, Man. , sponsored by the Dept. of Applied Mathematics, University of Manitoba"]
[{"$nulls":4},"and the Canadian Applied Mathematics Society."]
[null,"2.","Record Nr.",null,"UNINA9910338230703321"]
[null,null,"Autore",null,"Carter Eric"]
[null,null,"Titolo",null,"Agile Machine Learning : Effective Machine Learning Inspired by the Agile Manifesto / / by Eric Carter, Matthew Hurst"]
[null,null,"Pubbl/distr/stampa",null,"Berkeley, CA : , : Apress : , : Imprint : Apress, , 2019"]
[null,null,"ISBN",null,"9781484251072"]
[{"$nulls":4},"1484251075"]
[null,null,"Edizione",null,"[1st ed. 2019.]"]
[null,null,"Descrizione fisica",null,"1 online resource (257 pages) : illustrations"]
[null,null,"Disciplina",null,"004.165"]
[null,null,"Soggetti",null,"Microsoft software"]
[{"$nulls":4},"Microsoft .NET Framework"]
[{"$nulls":4},"Software engineering"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Microsoft and .NET"]
[{"$nulls":4},"Software Engineering"]
[{"$nulls":4},"Big Data"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Chapter 1: Early Delivery -- Chapter 2: Changing Requirements -- Chapter 3: Continuous Delivery -- Chapter 4: Aligning with the Business -- Chapter 5: Motivated Individuals -- Chapter 6: Effective Communication -- Chapter 7: Monitoring -- Chapter 8: Sustainable Development -- Chapter 9: Technical Excellence -- Chapter 10 Simplicity -- Chapter 11: Self-organizing Teams -- Chapter 12: Tuning and Adjusting -- Chapter 13: Conclusion."]
[null,null,null,"Sommario/riassunto","Build resilient applied machine learning teams that deliver better data products through adapting the guiding principles of the Agile Manifesto. Bringing together talented people to create a great applied machine learning team is no small feat. With developers and data scientists both contributing expertise in their respective fields, communication alone can be a challenge. Agile Machine Learning teaches you how to deliver superior data products through agile processes and to learn, by example, how to organize and manage a fast-paced team challenged with solving novel data problems at scale, in a production environment. The authors’ approach models the ground-breaking engineering principles described in the Agile "]
[{"$nulls":4},"Manifesto. The book provides further context, and contrasts the original principles with the requirements of systems that deliver a data product. What You'll Learn: Effectively run a data engineering team that is metrics-focused, experiment-focused, and data-focused Make sound implementation and model exploration decisions based on the data and the metrics Know the importance of data wallowing: analyzing data in real time in a group setting Recognize the value of always being able to measure your current state objectively Understand data literacy, a key attribute of a reliable data engineer, from definitions to expectations This book is for anyone who manages a machine learning team, or is responsible for creating production-ready inference components. Anyone responsible for data project workflow of sampling data; labeling, training, testing, improving, and maintaining models; and system and data metrics will also find this book useful. Readers should be familiar with software engineering and understand the basics of machine learning and working with data."]