LEADER 03002nam 2200457 450 001 9910831079803321 005 20210610080755.0 010 $a1-119-55672-4 010 $a1-119-55673-2 010 $a1-119-55674-0 035 $a(CKB)4100000008965339 035 $a(MiAaPQ)EBC5849505 035 $a(PPN)272715654 035 $a(CaSebORM)9781119556718 035 $a(OCoLC)1117320705 035 $a(EXLCZ)994100000008965339 100 $a20190917d2019 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMachine learning in the AWS Cloud $eadd intelligence to applications with Amazon SageMaker and Amazon Rekognition /$fAbhishek Mishra 205 $a1st edition 210 1$aIndianapolis, Indiana :$cSybex,$d[2019] 210 4$dİ2019 215 $a1 online resource (530 pages) 311 $a1-119-55671-6 320 $aIncludes bibliographical references and index. 330 $aPut the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You?ll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you?ll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. ?    Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building ?    Discover common neural network frameworks with Amazon SageMaker ?    Solve computer vision problems with Amazon Rekognition ?    Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem. 606 $aMachine learning 615 0$aMachine learning. 676 $a006.31 700 $aMishra$b Abhishek$0887481 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831079803321 996 $aMachine learning in the AWS Cloud$94029720 997 $aUNINA