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Learn Amazon SageMaker
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Quicklybuildanddeploymachinelearningmodelswithoutmanaginginfrastructure,andimproveproductivityusingAmazonSageMaker’scapabilitiessuchasAmazonSageMakerStudio,Autopilot,Experiments,Debugger,andModelMonitorKeyFeatures*Build,train,anddeploymachinelearningmodelsquicklyusingAmazonSageMaker*Analyze,detect,andreceivealertsrelatingtovariousbusinessproblemsusingmachinelearningalgorithmsandtechniques*Improveproductivitybytrainingandfine-tuningmachinelearningmodelsinproductionBookDescriptionAmazonSageMakerenablesyoutoquicklybuild,train,anddeploymachinelearning(ML)modelsatscale,withoutmanaginganyinfrastructure.IthelpsyoufocusontheMLproblemathandanddeployhigh-qualitymodelsbyremovingtheheavyliftingtypicallyinvolvedineachstepoftheMLprocess.ThisbookisacomprehensiveguidefordatascientistsandMLdeveloperswhowanttolearntheinsandoutsofAmazonSageMaker.You’llunderstandhowtousevariousmodulesofSageMakerasasingletoolsettosolvethechallengesfacedinML.Asyouprogress,you’llcoverfeaturessuchasAutoML,built-inalgorithmsandframeworks,andtheoptionforwritingyourowncodeandalgorithmstobuildMLmodels.Later,thebookwillshowyouhowtointegrateAmazonSageMakerwithpopulardeeplearninglibrariessuchasTensorFlowandPyTorchtoincreasethecapabilitiesofexistingmodels.You’llalsolearntogetthemodelstoproductionfasterwithminimumeffortandatalowercost.Finally,you’llexplorehowtouseAmazonSageMakerDebuggertoanalyze,detect,andhighlightproblemstounderstandthecurrentmodelstateandimprovemodelaccuracy.BytheendofthisAmazonbook,you’llbeabletouseAmazonSageMakeronthefullspectrumofMLworkflows,fromexperimentation,training,andmonitoringtoscaling,deployment,andautomation.Whatyouwilllearn*Createandautomateend-to-endmachinelearningworkflowsonAmazonWebServices(AWS)*Becomewell-versedwithdataannotationandpreparationtechniques*UseAutoMLfeaturestobuildandtrainmachinelearningmodelswithAutoPilot*Createmodelsusingbuilt-inalgorithmsandframeworksandyourowncode*TraincomputervisionandNLPmodelsusingreal-worldexamples*Covertrainingtechniquesforscaling,modeloptimization,modeldebugging,andcostoptimization*AutomatedeploymenttasksinavarietyofconfigurationsusingSDKandseveralautomationtoolsWhothisbookisforThisbookisforsoftwareengineers,machinelearningdevelopers,datascientists,andAWSuserswhoarenewtousingAmazonSageMakerandwanttobuildhigh-qualitymachinelearningmodelswithoutworryingaboutinfrastructure.KnowledgeofAWSbasicsisrequiredtograsptheconceptscoveredinthisbookmoreeffectively.SomeunderstandingofmachinelearningconceptsandthePythonprogramminglanguagewillalsobebeneficial.

Julien Simon;Francesco Pochetti ·統計 ·10.1萬字

大數據搜索與挖掘及可視化管理方案 :Elastic Stack 5:Elasticsearch、Logstash、Kibana、X-Pack、Beats (第3版)
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對大數據的搜索、挖掘、可視化以及集群管理,在當今的“互聯網+”時代是很有必要的。本書的分布式大數據搜索、日志挖掘、可視化、集群監控與管理等方案是基于ElasticStack5而提出的,它能有效應對海量大數據所帶來的分布式數據存儲與處理、全文檢索、日志挖掘、可視化、集群管理與性能監控等問題。構建在全文檢索開源軟件Lucene之上的Elasticsearch,不僅能對海量規模的數據完成分布式索引與檢索,還能提供數據聚合分析;Logstash能有效處理來源于各種數據源的日志信息;Kibana是為Elasticsearch提供數據分析的Web接口,可使用它對數據進行高效的搜索、可視化、分析等操作;XPack監控組件可通過Kibana監控集群的狀態;Beats是采集系統監控數據的代理。了解基于ElasticStack5的各相關組件并掌握它們的基本使用方法和技巧,對于大數據搜索與挖掘及管理是很有必要的。和第1版、第2版相比,本書力求反映基于ElasticStack5架構的最新成果,內容新穎,強調實踐。本書可為高等學校相關專業(如計算機科學與技術、軟件工程、物聯網、信息管理與信息系統、數據科學與大數據技術)學生的學習和科研工作提供幫助,同時對于從事大數據搜索與挖掘、日志分析、信息可視化、集群管理與性能監控的工程技術人員和希望了解網絡信息檢索技術的人員也具有較高的參考價值和工程應用價值。

高莘 ·統計 ·11.8萬字

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