LEADER 03589nam 22005415 450 001 9910900179903321 005 20241026125744.0 010 $a3-031-70008-2 024 7 $a10.1007/978-3-031-70008-8 035 $a(MiAaPQ)EBC31741924 035 $a(Au-PeEL)EBL31741924 035 $a(CKB)36410317800041 035 $a(DE-He213)978-3-031-70008-8 035 $a(EXLCZ)9936410317800041 100 $a20241026d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aTheory and Practice of Quality Assurance for Machine Learning Systems $eAn Experiment-Driven Approach /$fby Samuel Ackerman, Guy Barash, Eitan Farchi, Orna Raz, Onn Shehory 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (187 pages) 311 $a3-031-70007-4 327 $a1. Introduction -- 2. Scientific Analysis of ML Systems -- 3. Motivation and Best Practices for Machine Learning Designers and Testers -- 4. Unit Test vs. System Test of ML Based Systems -- 5. ML Testing -- 6. Principles of Drift Detection and ML Solution Retraining -- 7. Drift Detection by Measuring Distribution Differences -- 8. Sequential Drift Detection -- 9. Drift in Characterizations of Data -- 10. A Framework Analysis for Alternating Components and Drift -- 11. Optimal Integration of the ML Solution in the Business Decision Process -- 12. Testing Solutions Based on Large Language Models -- 13. A Detailed Chatbot Example. 330 $aThis book is a self-contained introduction to engineering and testing machine learning (ML) systems. It systematically discusses and teaches the art of crafting and developing software systems that include and surround machine learning models. Crafting ML based systems that are business-grade is highly challenging, as it requires statistical control throughout the complete system development life cycle. To this end, the book introduces an ?experiment first? approach, stressing the need to define statistical experiments from the beginning of the development life cycle and presenting methods for careful quantification of business requirements and identification of key factors that impact business requirements. Applying these methods reduces the risk of failure of an ML development project and of the resultant, deployed ML system. The presentation is complemented by numerous best practices, case studies and practical as well as theoretical exercises and their solutions, designed to facilitate understanding of the ideas, concepts and methods introduced. The goal of this book is to empower scientists, engineers, and software developers with the knowledge and skills necessary to create robust and reliable ML software. 606 $aSoftware engineering 606 $aArtificial intelligence 606 $aSoftware Engineering 606 $aArtificial Intelligence 615 0$aSoftware engineering. 615 0$aArtificial intelligence. 615 14$aSoftware Engineering. 615 24$aArtificial Intelligence. 676 $a005.1 700 $aAckerman$b Samuel$01767265 701 $aBarash$b Guy$01767266 701 $aFarchi$b Eitan$01767267 701 $aRaz$b Orna$01767268 701 $aShehory$b Onn$01767269 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910900179903321 996 $aTheory and Practice of Quality Assurance for Machine Learning Systems$94212266 997 $aUNINA