LEADER 00000cim a22004937i 4500 
003    OCoLC 
005    20240129213017.0 
006    m     o  h         
006    m     o  h         
007    sz zunnnnnuneu 
007    cr nnannnuuuuu 
008    221108s2022    xx nnnn o      i  n eng d 
020    9781837632459|q(electronic audio bk.) 
020    1837632456|q(electronic audio bk.) 
029 1  AU@|b000072937680 
035    (OCoLC)1350487496 
037    9781837632459|bO'Reilly Media 
040    ORMDA|beng|erda|epn|cORMDA|dOCLCF|dOCLCO 
049    INap 
082 04 006.31 
082 04 006.31|223/eng/20221108 
099    eAudiobook O’Reilly for Public Libraries 
100 1  Ping, David,|eauthor. 
245 14 The machine learning solutions architect handbook :
       |bcreate machine learning platforms to run solutions in an
       enterprise setting /|cDavid Ping.|h[O'Reilly for 
       electronic resources] 
250    [First edition]. 
264  1 [Place of publication not identified] :|bPackt Publishing,
       |c2022. 
300    1 online resource (1 audio file (08 hr., 32 min.)) 
306    083200 
336    spoken word|bspw|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
344    digital|2rdatr 
347    audio file|2rdaft 
520    Build highly secure and scalable machine learning 
       platforms to support the fast-paced adoption of machine 
       learning solutions About This Audiobook Explore different 
       ML tools and frameworks to solve large-scale machine 
       learning challenges in the cloud Build an efficient data 
       science environment for data exploration, model building, 
       and model training Learn how to implement bias detection, 
       privacy, and explainability in ML model development In 
       Detail When equipped with a highly scalable machine 
       learning (ML) platform, organizations can quickly scale 
       the delivery of ML products for faster business value 
       realization. There is a huge demand for skilled ML 
       solutions architects in different industries, and this 
       handbook will help you master the design patterns, 
       architectural considerations, and the latest technology 
       insights you'll need to become one. You'll start by 
       understanding ML fundamentals and how ML can be applied to
       solve real-world business problems. Once you've explored a
       few leading problem-solving ML algorithms, this book will 
       help you tackle data management and get the most out of ML
       libraries such as TensorFlow and PyTorch. Using open 
       source technology such as Kubernetes/Kubeflow to build a 
       data science environment and ML pipelines will be covered 
       next, before moving on to building an enterprise ML 
       architecture using Amazon Web Services (AWS). You'll also 
       learn about security and governance considerations, 
       advanced ML engineering techniques, and how to apply bias 
       detection, explainability, and privacy in ML model 
       development. And finally, you'll get acquainted with AWS 
       AI services and their applications in real-world use 
       cases. By the end of this audiobook, you'll be able to 
       design and build an ML platform to support common use 
       cases and architecture patterns like a true professional. 
       Audience This audiobook is for data scientists, data 
       engineers, cloud architects, and machine learning 
       enthusiasts who want to become machine learning solutions 
       architects. You'll need basic knowledge of the Python 
       programming language, AWS, linear algebra, probability, 
       and networking concepts before you get started with this 
       handbook. 
588 0  Online resource; title from title details screen (O'Reilly,
       viewed November 8, 2022). 
590    O'Reilly|bO'Reilly Online Learning: Academic/Public 
       Library Edition 
650  0 Machine learning. 
650  6 Apprentissage automatique. 
650  7 Machine learning|2fast 
655  7 Audiobooks|2fast 
655  7 Audiobooks.|2lcgft 
655  7 Livres audio.|2rvmgf 
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
       learning.oreilly.com/library/view/~/9781837632459/?ar
       |zAvailable on O’Reilly for Public Libraries 
994    92|bJFN