LEADER 04311nam 22006135 450 001 9910338230703321 005 20200630072717.0 010 $a1-4842-5107-5 024 7 $a10.1007/978-1-4842-5107-2 035 $a(CKB)4100000009076161 035 $a(MiAaPQ)EBC5879995 035 $a(DE-He213)978-1-4842-5107-2 035 $a(CaSebORM)9781484251072 035 $a(PPN)248605003 035 $a(OCoLC)1122564749 035 $a(OCoLC)on1122564749 035 $a(EXLCZ)994100000009076161 100 $a20190821d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aAgile Machine Learning $eEffective Machine Learning Inspired by the Agile Manifesto /$fby Eric Carter, Matthew Hurst 205 $a1st ed. 2019. 210 1$aBerkeley, CA :$cApress :$cImprint: Apress,$d2019. 215 $a1 online resource (257 pages) $cillustrations 300 $aIncludes index. 311 $a1-4842-5106-7 327 $aChapter 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. 330 $aBuild 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 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. 606 $aMicrosoft software 606 $aMicrosoft .NET Framework 606 $aSoftware engineering 606 $aBig data 606 $aMicrosoft and .NET$3https://scigraph.springernature.com/ontologies/product-market-codes/I29030 606 $aSoftware Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/I14029 606 $aBig Data$3https://scigraph.springernature.com/ontologies/product-market-codes/I29120 615 0$aMicrosoft software. 615 0$aMicrosoft .NET Framework. 615 0$aSoftware engineering. 615 0$aBig data. 615 14$aMicrosoft and .NET. 615 24$aSoftware Engineering. 615 24$aBig Data. 676 $a004.165 700 $aCarter$b Eric$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063749 702 $aHurst$b Matthew$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bUMI 801 1$bUMI 906 $aBOOK 912 $a9910338230703321 996 $aAgile Machine Learning$92534317 997 $aUNINA