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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(tǒng)計(jì) ·10.1萬字

大數(shù)據(jù)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(shù)據(jù)的搜索、挖掘、可視化以及集群管理,在當(dāng)今的“互聯(lián)網(wǎng)+”時代是很有必要的。本書的分布式大數(shù)據(jù)搜索、日志挖掘、可視化、集群監(jiān)控與管理等方案是基于ElasticStack5而提出的,它能有效應(yīng)對海量大數(shù)據(jù)所帶來的分布式數(shù)據(jù)存儲與處理、全文檢索、日志挖掘、可視化、集群管理與性能監(jiān)控等問題。構(gòu)建在全文檢索開源軟件Lucene之上的Elasticsearch,不僅能對海量規(guī)模的數(shù)據(jù)完成分布式索引與檢索,還能提供數(shù)據(jù)聚合分析;Logstash能有效處理來源于各種數(shù)據(jù)源的日志信息;Kibana是為Elasticsearch提供數(shù)據(jù)分析的Web接口,可使用它對數(shù)據(jù)進(jìn)行高效的搜索、可視化、分析等操作;XPack監(jiān)控組件可通過Kibana監(jiān)控集群的狀態(tài);Beats是采集系統(tǒng)監(jiān)控?cái)?shù)據(jù)的代理。了解基于ElasticStack5的各相關(guān)組件并掌握它們的基本使用方法和技巧,對于大數(shù)據(jù)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(gòu)的最新成果,內(nèi)容新穎,強(qiáng)調(diào)實(shí)踐。本書可為高等學(xué)校相關(guān)專業(yè)(如計(jì)算機(jī)科學(xué)與技術(shù)、軟件工程、物聯(lián)網(wǎng)、信息管理與信息系統(tǒng)、數(shù)據(jù)科學(xué)與大數(shù)據(jù)技術(shù))學(xué)生的學(xué)習(xí)和科研工作提供幫助,同時對于從事大數(shù)據(jù)搜索與挖掘、日志分析、信息可視化、集群管理與性能監(jiān)控的工程技術(shù)人員和希望了解網(wǎng)絡(luò)信息檢索技術(shù)的人員也具有較高的參考價值和工程應(yīng)用價值。

高莘 ·統(tǒng)計(jì) ·11.8萬字

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