000 06471cam a2200637Ii 4500
001 on1097972579
003 OCoLC
005 20220711203516.0
006 m o d
007 cr cnu---unuuu
008 190420s2019 inu ob 001 0 eng d
040 _aEBLCP
_beng
_erda
_epn
_cEBLCP
_dN$T
_dOCLCO
_dDG1
_dOCLCF
_dRECBK
_dCOO
_dOCLCQ
_dOH1
_dSTF
_dUPM
_dYDXIT
019 _a1097665075
_a1104315408
_a1104402145
020 _a9781119564874
_qelectronic book
020 _a1119564875
_qelectronic book
020 _a9781119564843
_qelectronic book
020 _a1119564840
_qelectronic book
020 _z9781119564836
_qpaperback
020 _z1119564832
_qpaperback
029 1 _aAU@
_b000065220400
029 1 _aCHNEW
_b001050921
029 1 _aCHVBK
_b567422747
035 _a(OCoLC)1097972579
_z(OCoLC)1097665075
_z(OCoLC)1104315408
_z(OCoLC)1104402145
050 4 _aQ325.5
_b.R36 2019
072 7 _aCOM
_x000000
_2bisacsh
082 0 4 _a006.3/1
_223
049 _aMAIN
100 1 _aRao, Dattaraj,
_eauthor.
_98350
245 1 0 _aKeras to Kubernetes :
_bthe journey of a machine learning model to production /
_cDattaraj Jagdish Rao.
264 1 _aIndianapolis, IN :
_bJohn Wiley & Sons,
_c[2019]
300 _a1 online resource (xviii, 302 pages)
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
588 _aDescription based on online resource; title from digital title page (viewed on August 08, 2019).
504 _aIncludes bibliographical references and index.
520 _aBuild a Keras model to scale and deploy on a Kubernetes cluster. We have seen an exponential growth in the use of Artificial Intelligence (AI) over last few years. AI is becoming the new electricity and is touching every industry from retail to manufacturing to healthcare to entertainment. Within AI, we're seeing a particular growth in Machine Learning (ML) and Deep Learning (DL) applications. ML is all about learning relationships from labeled (Supervised) or unlabeled data (Unsupervised). DL has many layers of learning and can extract patterns from unstructured data like images, video, audio, etc. Keras to Kubernetes: The Journey of a Machine Learning Model to Production takes you through real-world examples of building DL models in Keras for recognizing product logos in images and extracting sentiment from text. You will then take that trained model and package it as a web application container before learning how to deploy this model at scale on a Kubernetes cluster. You will understand the different practical steps involved in real-world ML implementations which go beyond the algorithms.
505 0 _aCover; Title Page; Copyright; Acknowledgments; About the Author; About the Technical Editor; Credits; Contents; Introduction; How This Book Is Organized; Conventions Used; Who Should Read This Book; Tools You Will Need; Using Python; Using the Frameworks; Setting Up a Notebook; Finding a Dataset; Summary; Chapter 1 Big Data and Artificial Intelligence; Data Is the New Oil and AI Is the New Electricity; Rise of the Machines; Exponential Growth in Processing; A New Breed of Analytics; What Makes AI So Special; Applications of Artificial Intelligence; Building Analytics on Data
505 8 _aTypes of Analytics: Based on the ApplicationTypes of Analytics: Based on Decision Logic; Building an Analytics-Driven System; Summary; Chapter 2 Machine Learning; Finding Patterns in Data; The Awesome Machine Learning Community; Types of Machine Learning Techniques; Unsupervised Machine Learning; Supervised Machine Learning; Reinforcement Learning; Solving a Simple Problem; Unsupervised Learning; Supervised Learning: Linear Regression; Gradient Descent Optimization; Applying Gradient Descent to Linear Regression; Supervised Learning: Classification; Analyzing a Bigger Dataset
505 8 _aMetrics for Accuracy: Precision and RecallComparison of Classification Methods; Bias vs. Variance: Underfitting vs. Overfitting; Reinforcement Learning; Model-Based RL; Model-Free RL; Summary; Chapter 3 Handling Unstructured Data; Structured vs. Unstructured Data; Making Sense of Images; Dealing with Videos; Handling Textual Data; Listening to Sound; Summary; Chapter 4 Deep Learning Using Keras; Handling Unstructured Data; Neural Networks; Back-Propagation and Gradient Descent; Batch vs. Stochastic Gradient Descent; Neural Network Architectures; Welcome to TensorFlow and Keras
505 8 _aBias vs. Variance: Underfitting vs. OverfittingSummary; Chapter 5 Advanced Deep Learning; The Rise of Deep Learning Models; New Kinds of Network Layers; Convolution Layer; Pooling Layer; Dropout Layer; Batch Normalization Layer; Building a Deep Network for Classifying Fashion Images; CNN Architectures and Hyper-Parameters; Making Predictions Using a Pretrained VGG Model; Data Augmentation and Transfer Learning; A Real Classification Problem: Pepsi vs. Coke; Recurrent Neural Networks; Summary; Chapter 6 Cutting-Edge Deep Learning Projects; Neural Style Transfer; Generating Images Using AI
505 8 _aCredit Card Fraud Detection with AutoencodersSummary; Chapter 7 AI in the Modern Software World; A Quick Look at Modern Software Needs; How AI Fits into Modern Software Development; Simple to Fancy Web Applications; The Rise of Cloud Computing; Containers and CaaS; Microservices Architecture with Containers; Kubernetes: A CaaS Solution for Infrastructure Concerns; Summary; Chapter 8 Deploying AI Models as Microservices; Building a Simple Microservice with Docker and Kubernetes; Adding AI Smarts to Your App; Packaging the App as a Container; Pushing a Docker Image to a Repository
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 7 _aCOMPUTERS
_xGeneral.
_2bisacsh
_94629
650 7 _aArtificial intelligence.
_2fast
_0(OCoLC)fst00817247
_93407
650 7 _aBig data.
_2fast
_0(OCoLC)fst01892965
_94174
650 7 _aData mining.
_2fast
_0(OCoLC)fst00887946
_93907
650 7 _aMachine learning.
_2fast
_0(OCoLC)fst01004795
_91831
655 0 _aElectronic books.
_93294
655 4 _aElectronic books.
_93294
776 0 8 _iPrint version:
_aRao, Dattaraj.
_tKeras to Kubernetes : The Journey of a Machine Learning Model to Production.
_dNewark : John Wiley & Sons, Incorporated, ©2019
_z9781119564836
856 4 0 _uhttps://doi.org/10.1002/9781119564843
_zWiley Online Library
942 _cEBK
994 _a92
_bDG1
999 _c69072
_d69072