{"product_id":"aws-certified-machine-learning-study-guide-specialty-mls-c01-exam-paperback-softback-9781119821007","title":"AWS Certified Machine Learning Study Guide; Specialty (MLS-C01) Exam (Paperback \/ softback) 9781119821007","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eAWS Certified Machine Learning Study Guide\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eSpecialty (MLS-C01) Exam\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eShreyas Subramanian (Author), Stefan Natu (Author)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781119821007, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003ePaperback \/ softback, published 7 February 2022\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e352 pages\u003cbr\u003e23.1 x 18.8 x 2.3 cm, 0.499 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\u003cb\u003eSucceed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide \u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAs the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. \u003c\/p\u003e \u003cp\u003eThe \u003ci\u003eAWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam\u003c\/i\u003e delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. \u003c\/p\u003e \u003cp\u003eFrom exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. \u003c\/p\u003e \u003cp\u003eYou’ll also find: \u003c\/p\u003e \u003cul\u003e \u003cli\u003eAn intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud \u003c\/li\u003e \u003cli\u003eAuthoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science \u003c\/li\u003e \u003cli\u003eAccess to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eAWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam\u003c\/i\u003e is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning. \u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eIntroduction xvii\u003c\/p\u003e \u003cp\u003eAssessment Test xxix\u003c\/p\u003e \u003cp\u003eAnswers to Assessment Test xxxv\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart I Introduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 AWS AI ML Stack 3\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAmazon Rekognition 4\u003c\/p\u003e \u003cp\u003eImage and Video Operations 6\u003c\/p\u003e \u003cp\u003eAmazon Textract 10\u003c\/p\u003e \u003cp\u003eSync and Async APIs 11\u003c\/p\u003e \u003cp\u003eAmazon Transcribe 13\u003c\/p\u003e \u003cp\u003eTranscribe Features 13\u003c\/p\u003e \u003cp\u003eTranscribe Medical 14\u003c\/p\u003e \u003cp\u003eAmazon Translate 15\u003c\/p\u003e \u003cp\u003eAmazon Translate Features 16\u003c\/p\u003e \u003cp\u003eAmazon Polly 17\u003c\/p\u003e \u003cp\u003eAmazon Lex 19\u003c\/p\u003e \u003cp\u003eLex Concepts 19\u003c\/p\u003e \u003cp\u003eAmazon Kendra 21\u003c\/p\u003e \u003cp\u003eHow Kendra Works 22\u003c\/p\u003e \u003cp\u003eAmazon Personalize 23\u003c\/p\u003e \u003cp\u003eAmazon Forecast 27\u003c\/p\u003e \u003cp\u003eForecasting Metrics 30\u003c\/p\u003e \u003cp\u003eAmazon Comprehend 32\u003c\/p\u003e \u003cp\u003eAmazon CodeGuru 33\u003c\/p\u003e \u003cp\u003eAmazon Augmented AI 34\u003c\/p\u003e \u003cp\u003eAmazon SageMaker 35\u003c\/p\u003e \u003cp\u003eAnalyzing and Preprocessing Data 36\u003c\/p\u003e \u003cp\u003eTraining 39\u003c\/p\u003e \u003cp\u003eModel Inference 40\u003c\/p\u003e \u003cp\u003eAWS Machine Learning Devices 42\u003c\/p\u003e \u003cp\u003eSummary 43\u003c\/p\u003e \u003cp\u003eExam Essentials 43\u003c\/p\u003e \u003cp\u003eReview Questions 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Supporting Services from the AWS Stack 49\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStorage 50\u003c\/p\u003e \u003cp\u003eAmazon S3 50\u003c\/p\u003e \u003cp\u003eAmazon EFS 52\u003c\/p\u003e \u003cp\u003eAmazon FSx for Lustre 52\u003c\/p\u003e \u003cp\u003eData Versioning 53\u003c\/p\u003e \u003cp\u003eAmazon VPC 54\u003c\/p\u003e \u003cp\u003eAWS Lambda 56\u003c\/p\u003e \u003cp\u003eAWS Step Functions 59\u003c\/p\u003e \u003cp\u003eAWS RoboMaker 60\u003c\/p\u003e \u003cp\u003eSummary 62\u003c\/p\u003e \u003cp\u003eExam Essentials 62\u003c\/p\u003e \u003cp\u003eReview Questions 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart II Phases of Machine Learning Workloads 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Business Understanding 69\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePhases of ML Workloads 70\u003c\/p\u003e \u003cp\u003eBusiness Problem Identification 71\u003c\/p\u003e \u003cp\u003eSummary 72\u003c\/p\u003e \u003cp\u003eExam Essentials 73\u003c\/p\u003e \u003cp\u003eReview Questions 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Framing a Machine Learning Problem 77\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eML Problem Framing 78\u003c\/p\u003e \u003cp\u003eRecommended Practices 80\u003c\/p\u003e \u003cp\u003eSummary 81\u003c\/p\u003e \u003cp\u003eExam Essentials 81\u003c\/p\u003e \u003cp\u003eReview Questions 82\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Data Collection 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBasic Data Concepts 86\u003c\/p\u003e \u003cp\u003eData Repositories 88\u003c\/p\u003e \u003cp\u003eData Migration to AWS 89\u003c\/p\u003e \u003cp\u003eBatch Data Collection 89\u003c\/p\u003e \u003cp\u003eStreaming Data Collection 92\u003c\/p\u003e \u003cp\u003eSummary 96\u003c\/p\u003e \u003cp\u003eExam Essentials 96\u003c\/p\u003e \u003cp\u003eReview Questions 98\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Data Preparation 101\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData Preparation Tools 102\u003c\/p\u003e \u003cp\u003eSageMaker Ground Truth 102\u003c\/p\u003e \u003cp\u003eAmazon EMR 104\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Processing 105\u003c\/p\u003e \u003cp\u003eAWS Glue 105\u003c\/p\u003e \u003cp\u003eAmazon Athena 107\u003c\/p\u003e \u003cp\u003eRedshift Spectrum 107\u003c\/p\u003e \u003cp\u003eSummary 107\u003c\/p\u003e \u003cp\u003eExam Essentials 107\u003c\/p\u003e \u003cp\u003eReview Questions 109\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Feature Engineering 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFeature Engineering Concepts 114\u003c\/p\u003e \u003cp\u003eFeature Engineering for Tabular Data 114\u003c\/p\u003e \u003cp\u003eFeature Engineering for Unstructured and Time Series Data 119\u003c\/p\u003e \u003cp\u003eFeature Engineering Tools on AWS 120\u003c\/p\u003e \u003cp\u003eSummary 121\u003c\/p\u003e \u003cp\u003eExam Essentials 121\u003c\/p\u003e \u003cp\u003eReview Questions 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Model Training 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCommon ML Algorithms 128\u003c\/p\u003e \u003cp\u003eSupervised Machine Learning 129\u003c\/p\u003e \u003cp\u003eTextual Data 138\u003c\/p\u003e \u003cp\u003eImage Analysis 141\u003c\/p\u003e \u003cp\u003eUnsupervised Machine Learning 142\u003c\/p\u003e \u003cp\u003eReinforcement Learning 146\u003c\/p\u003e \u003cp\u003eLocal Training and Testing 147\u003c\/p\u003e \u003cp\u003eRemote Training 149\u003c\/p\u003e \u003cp\u003eDistributed Training 150\u003c\/p\u003e \u003cp\u003eMonitoring Training Jobs 154\u003c\/p\u003e \u003cp\u003eAmazon CloudWatch 155\u003c\/p\u003e \u003cp\u003eAWS CloudTrail 155\u003c\/p\u003e \u003cp\u003eAmazon Event Bridge 158\u003c\/p\u003e \u003cp\u003eDebugging Training Jobs 158\u003c\/p\u003e \u003cp\u003eHyperparameter Optimization 159\u003c\/p\u003e \u003cp\u003eSummary 162\u003c\/p\u003e \u003cp\u003eExam Essentials 162\u003c\/p\u003e \u003cp\u003eReview Questions 164\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Model Evaluation 167\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExperiment Management 168\u003c\/p\u003e \u003cp\u003eMetrics and Visualization 169\u003c\/p\u003e \u003cp\u003eMetrics in AWS AI\/ML Services 173\u003c\/p\u003e \u003cp\u003eSummary 174\u003c\/p\u003e \u003cp\u003eExam Essentials 175\u003c\/p\u003e \u003cp\u003eReview Questions 176\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Model Deployment and Inference 181\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDeployment for AI Services 182\u003c\/p\u003e \u003cp\u003eDeployment for Amazon SageMaker 184\u003c\/p\u003e \u003cp\u003eSageMaker Hosting: Under the Hood 184\u003c\/p\u003e \u003cp\u003eAdvanced Deployment Topics 187\u003c\/p\u003e \u003cp\u003eAutoscaling Endpoints 187\u003c\/p\u003e \u003cp\u003eDeployment Strategies 188\u003c\/p\u003e \u003cp\u003eTesting Strategies 190\u003c\/p\u003e \u003cp\u003eSummary 191\u003c\/p\u003e \u003cp\u003eExam Essentials 191\u003c\/p\u003e \u003cp\u003eReview Questions 192\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Application Integration 195\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIntegration with On-Premises\u003c\/p\u003e \u003cp\u003eSystems 196\u003c\/p\u003e \u003cp\u003eIntegration with Cloud Systems 198\u003c\/p\u003e \u003cp\u003eIntegration with Front-End\u003c\/p\u003e \u003cp\u003eSystems 200\u003c\/p\u003e \u003cp\u003eSummary 200\u003c\/p\u003e \u003cp\u003eExam Essentials 201\u003c\/p\u003e \u003cp\u003eReview Questions 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart III Machine Learning Well-Architected Lens 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Operational Excellence Pillar for ML 207\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOperational Excellence on AWS 208\u003c\/p\u003e \u003cp\u003eEverything as Code 209\u003c\/p\u003e \u003cp\u003eContinuous Integration and Continuous Delivery 210\u003c\/p\u003e \u003cp\u003eContinuous Monitoring 213\u003c\/p\u003e \u003cp\u003eContinuous Improvement 214\u003c\/p\u003e \u003cp\u003eSummary 215\u003c\/p\u003e \u003cp\u003eExam Essentials 215\u003c\/p\u003e \u003cp\u003eReview Questions 217\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Security Pillar 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSecurity and AWS 222\u003c\/p\u003e \u003cp\u003eData Protection 223\u003c\/p\u003e \u003cp\u003eIsolation of Compute 224\u003c\/p\u003e \u003cp\u003eFine-Grained\u003c\/p\u003e \u003cp\u003eAccess Controls 225\u003c\/p\u003e \u003cp\u003eAudit and Logging 226\u003c\/p\u003e \u003cp\u003eCompliance Scope 227\u003c\/p\u003e \u003cp\u003eSecure SageMaker Environments 228\u003c\/p\u003e \u003cp\u003eAuthentication and Authorization 228\u003c\/p\u003e \u003cp\u003eData Protection 231\u003c\/p\u003e \u003cp\u003eNetwork Isolation 232\u003c\/p\u003e \u003cp\u003eLogging and Monitoring 233\u003c\/p\u003e \u003cp\u003eCompliance Scope 235\u003c\/p\u003e \u003cp\u003eAI Services Security 235\u003c\/p\u003e \u003cp\u003eSummary 236\u003c\/p\u003e \u003cp\u003eExam Essentials 236\u003c\/p\u003e \u003cp\u003eReview Questions 238\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Reliability Pillar 241\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReliability on AWS 242\u003c\/p\u003e \u003cp\u003eChange Management for ML 242\u003c\/p\u003e \u003cp\u003eFailure Management for ML 245\u003c\/p\u003e \u003cp\u003eSummary 246\u003c\/p\u003e \u003cp\u003eExam Essentials 246\u003c\/p\u003e \u003cp\u003eReview Questions 247\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Performance Efficiency Pillar for ML 251\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePerformance Efficiency for ML on AWS 252\u003c\/p\u003e \u003cp\u003eSelection 253\u003c\/p\u003e \u003cp\u003eReview 254\u003c\/p\u003e \u003cp\u003eMonitoring 255\u003c\/p\u003e \u003cp\u003eTrade-offs\u003c\/p\u003e \u003cp\u003e256\u003c\/p\u003e \u003cp\u003eSummary 257\u003c\/p\u003e \u003cp\u003eExam Essentials 257\u003c\/p\u003e \u003cp\u003eReview Questions 258\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Cost Optimization Pillar for ML 261\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCommon Design Principles 262\u003c\/p\u003e \u003cp\u003eCost Optimization for ML Workloads 263\u003c\/p\u003e \u003cp\u003eDesign Principles 263\u003c\/p\u003e \u003cp\u003eCommon Cost Optimization Strategies 264\u003c\/p\u003e \u003cp\u003eSummary 266\u003c\/p\u003e \u003cp\u003eExam Essentials 266\u003c\/p\u003e \u003cp\u003eReview Questions 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Recent Updates in the AWS AI\/ML Stack 271\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eNew Services and Features Related to AI Services 272\u003c\/p\u003e \u003cp\u003eNew Services 272\u003c\/p\u003e \u003cp\u003eNew Features of Existing Services 275\u003c\/p\u003e \u003cp\u003eNew Features Related to Amazon SageMaker 279\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Studio 279\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Data Wrangler 279\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Feature Store 280\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Clarify 281\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Autopilot 282\u003c\/p\u003e \u003cp\u003eAmazon SageMaker JumpStart 283\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Debugger 283\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Distributed Training Libraries 284\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Pipelines and Projects 284\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Model Monitor 284\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Edge Manager 285\u003c\/p\u003e \u003cp\u003eAmazon SageMaker Asynchronous Inference 285\u003c\/p\u003e \u003cp\u003eSummary 285\u003c\/p\u003e \u003cp\u003eExam Essentials 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAppendix Answers to the Review Questions 287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eChapter 1: AWS AI ML Stack 288\u003c\/p\u003e \u003cp\u003eChapter 2: Supporting Services from the AWS Stack 289\u003c\/p\u003e \u003cp\u003eChapter 3: Business Understanding 290\u003c\/p\u003e \u003cp\u003eChapter 4: Framing a Machine Learning Problem 291\u003c\/p\u003e \u003cp\u003eChapter 5: Data Collection 291\u003c\/p\u003e \u003cp\u003eChapter 6: Data Preparation 292\u003c\/p\u003e \u003cp\u003eChapter 7: Feature Engineering 293\u003c\/p\u003e \u003cp\u003eChapter 8: Model Training 294\u003c\/p\u003e \u003cp\u003eChapter 9: Model Evaluation 295\u003c\/p\u003e \u003cp\u003eChapter 10: Model Deployment and Inference 295\u003c\/p\u003e \u003cp\u003eChapter 11: Application Integration 296\u003c\/p\u003e \u003cp\u003eChapter 12: Operational Excellence Pillar for ML 297\u003c\/p\u003e \u003cp\u003eChapter 13: Security Pillar 298\u003c\/p\u003e \u003cp\u003eChapter 14: Reliability Pillar 298\u003c\/p\u003e \u003cp\u003eChapter 15: Performance Efficiency Pillar for ML 299\u003c\/p\u003e \u003cp\u003eChapter 16: Cost Optimization Pillar for ML 300\u003c\/p\u003e \u003cp\u003eIndex 303\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Electronics \u0026amp; communications engineering [\u003ca title=\"See our other books on Electronics \u0026amp; communications engineering\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Electronics%20\u0026amp;%20communications%20engineering%20%5BTJ%5D%22\"\u003eTJ\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Sybex","offers":[{"title":"Brand New","offer_id":52460691554584,"sku":"9781119821007","price":32.57,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781119821007.jpg?v=1785458432","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/aws-certified-machine-learning-study-guide-specialty-mls-c01-exam-paperback-softback-9781119821007","provider":"Freshly Printed Books","version":"1.0","type":"link"}