使用H2O Driverless实现机器学习自动化戴尔基础设施上的人工智能

AutomateMachineLearningwithH2ODriverlessAIonDellInfrastructure

DellValidatedDesignforAI

July2022H19252

WhitePaper

Abstract

Thistechnicalwhitepaperdiscussesthebenefitsofautomatedmachinelearningandthechallengesofnon-automatedmodeldevelopmentthatitovercomes.ThepaperpresentsanoverviewoftheH2ODriverlessAIproductfromH2O.ai,alongwithasolutionarchitectureforH2ODriverlessAIbuiltontheDellValidatedDesignforAI.Italsoprovidesseveralvalidatedusecasesusingthesolution.

DellTechnologiesSolutions

Copyright

Contents

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Theinformationinthispublicationisprovidedasis.DellInc.makesnorepresentationsorwarrantiesofanykindwithrespecttotheinformationinthispublication,andspecificallydisclaimsimpliedwarrantiesofmerchantabilityorfitnessforaparticularpurpose.

Use,copying,anddistributionofanysoftwaredescribedinthispublicationrequiresanapplicablesoftwarelicense.Copyright©2022DellInc.oritssubsidiaries.PublishedintheUSA07/22.WhitePaperH19252.

DellInc.believestheinformationinthisdocumentisaccurateasofitspublicationdate.Theinformationissubjecttochangewithoutnotice.

Contents

Introduction 5

Executivesummary 5

Documentpurpose 6

Audience 6

ThechallengesofAIadoption 6

Machinelearningchallenges 6

Talent 6

Time 6

Trust 7

OverviewofAutoMLandH2ODriverlessAI 7

AutoMLworkflowwithH2ODriverlessAI 7

Keyfeatures 10

SolutionarchitectureforAutoML 11

Kubernetes-baseddeploymentusingEnterpriseSteam 11

Dockerimage 12

Security 12

GPUsupport 12

Storageandnetworkconfiguration 13

Licensing 13

InvokingH2ODriverlessAIfromcnvrg.ioMLOpsPlatform 13

AutoMLonanoptimizedDellinfrastructure 15

SizingofAutoMLinfrastructure 16

ValidatedusecasesforAutoML 17

SentimentanalysiswithNLP 17

Imageclassification 20

DellTechnologiesservicesandsupport 21

Deploymentandsupport 21

TheDellTechnologiesCustomerSolutionsCenter 22

Conclusion 22

Wevalueyourfeedback 23

References 24

Contents

Introduction

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DellTechnologiesdocumentation 24

H2O.aidocumentation 24

NVIDIAdocumentation 24

AppendixA–Modelservingincnvrg.io 25

Introduction

Executivesummary

Artificialintelligence(AI)andmachinelearninghaverevolutionizedhoworganizationsareusingtheirdata.Automatedmachinelearning(AutoML)facilitatesandimprovestheend-to-enddatascienceprocess.Thisprocessincludeseverythingfrompreprocessingandcleaningthedata,selectingandengineeringappropriatefeatures,tuningandoptimizingthemodel,analyzingresults,explaininganddocumentingthemodel,andofcourse,deployingitintoproduction.

AutoMLacceleratesyourAIinitiativesbyprovidingmethodsandprocessestomakemachinelearningaccessibletobothexpertsandnonexpertsalike.OrganizationslookingtoapplymachinelearningquicklyandaccuratelywithoutemployinglargenumbersofdatascientistscanbenefitfromAutoMLcapabilities.Fororganizationsthathavedatascientists,AutoMLequipsandempowersthemtocreatemorerobustmodelswithaccuracy,speed,andtransparencytodeliverbetterperformanceandoutcomes.Inallcases,AutoMLhelpsorganizationsquicklydiscoverbusinessvaluehiddeninsidetheirdataandeasilyusethatdatatoaddresscomplexproblems.

H2ODriverlessAIisacomprehensiveautomatedmachinelearningproductthatusesAItodoAI,optimizingdatascienceworkflowstoincreaseboththequantityandqualityofdatascienceprojectsdeliveredtobusinessstakeholders.Itempowersdatascientiststoworkonprojectsfasterandmoreefficientlybyusingautomationtoaccomplishkeymachinelearningtasksinminutesorhours,notmonths.

H2ODriverlessAIprovidescapabilitiessuchas:

Exploratorydataanalysis(AutoViz)

Automaticfeatureengineering

Modelbuildingandvalidation

Automaticmodeldocumentation(AutoDoc)

Modelselectionanddeployment

Machinelearninginterpretability(MLI)

AutoMLdoesnotreplacemachinelearningoperations(MLOps).AutoMLfocusesonautomatingandacceleratingthemodeldevelopmentportionoftheMLpipeline,whileMLOpsprovidesanoveralllifecyclemanagementframeworkfordatapreparation,modeldevelopment,andcoding.AutoMLcomplementsMLOpsandcanrunsuccessfullyandefficientlywithvariousMLOpsframeworkssuchascnvrg.io.MLOpsprovidesanoveralllifecyclemanagementframeworkfordatapreparation,modeldevelopment,andcoding.

WithH2ODriverlessAIbring-your-ownrecipes,andtimeseriesandautomaticpipelinegenerationformodelscoring,H2ODriverlessAIprovidescompanieswithanextensibleandcustomizabledatascienceplatformthataddressestheneedsofvarioususecasesforeveryenterpriseineveryindustry.

ThechallengesofAIadoption

OverviewofAutoMLandH2ODriverlessAI

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Documentpurpose

Audience

ThiswhitepaperdiscussesAutoML,includingitsbenefitsandthechallengesofmoretraditionalmodeldevelopmentprocessesthatitovercomes.ThewhitepaperprovidesanoverviewoftheH2ODriverlessAIproduct,presentsasolutionarchitectureforH2ODriverlessAIbuiltontheDellValidatedDesignforAIwithVMware,anddescribesseveralvalidatedusecasesusingthesolution.Bydeployingthissolution,datascientistsandITprofessionalscanmovemachinelearningmodelsoutofthelabandintoproductionfasterandmoreeasily,thusbringingabetterreturnoninvestment(ROI)foranorganization’smachinelearninginvestments.

Thiswhitepaperisintendedfordatascientists,solutionarchitects,systemadministrators,andothersdevelopingandsupportingAIandmachinelearningapplications.

ThechallengesofAIadoption

Machinelearningchallenges

AsorganizationsstreamlinedecisionmakingandimprovecustomerexperienceswithAI,theyarerunningintothreecorechallenges:talent,time,andtrust.First,thereisnotenoughdatasciencetalenttobuildmodelsforeveryusecasebyhand.Evenwiththerightpeople,hand-codingtakestoomuchtimeandispronetoerrors.Then,thebusinessmustexplainandvalidateeachmodelsothatuserscantrustthedecisionsthatthemodelsupports.Thekeytobreakingthroughthetalent,time,andtrustbarriersistheautomationofadvancedmachinelearningtechniqueswithH2ODriverlessAI.

Talent

Datascientistsareinshortsupplyforallbutthelargesttechnologycompanies.WithH2ODriverlessAI,bothexpertandnovicedatascientistscanautomaticallybuildhighlyandtransparentaccuratemodelsquickly.H2ODriverlessAIisanaward-winningAutoMLproductthatembedsdatasciencebestpracticesfromtheworld’sleadingexpertsinengineeringanddatascience,includingtheworld’stopKaggleGrandmasters.Itusesauniquegeneticalgorithmthatdeterminesthebestcombinationoffeatures,models,andtuningparametersforeachusecase.Integratedbestpracticesandguardrailsensurethatmodelsdonotoverfitthedataandhelpwithothercommonissueswithwhichnovicedatascientistsmightneedassistance.H2ODriverlessAIenablescompaniestoundertakemoreusecaseswiththetalentthattheyalreadyhaveorcaneasilyfind.

Time

Reducingthetimetodevelopaccurate,production-readymodelsiscriticaltodeliveringAIatscale.H2ODriverlessAIautomatestime-consumingdatasciencetaskssuchasadvancedfeatureengineering,modelselection,hyperparametertuning,modelstacking,andcreationofaneasy-to-deploy,low-latencyscoringpipeline.Withhigh-performancecomputingusingbothCPUsandGPUs,H2ODriverlessAIcomparesthousandsofcombinationsanditerationstofindthebestmodelinminutesorhours.EvenexperienceddatascientistscanuseH2ODriverlessAItoexploremoretechniques,featurecombinations,andtuningparameters.H2ODriverlessAIalsostreamlinesmodeldeploymentthatincludeseverythingneededtorunthemodelinproduction,takingtheprocesstimefromexperimentationtoproductionfrommonthstodays.

Trust

FororganizationstoadoptAIatscale,datateams,businessleaders,andregulatorsmustbeabletoexplain,interpret,andtrustAIresults.H2ODriverlessAIdeliversindustry-leadingcapabilitiesforunderstanding,debugging,andsharingmodelresults,includinganextensivemachinelearninginterpretability(MLI)toolkit,fairnessdashboards,automatedmodeldocumentation,andreasoncodesforeachpredictionforservicerepresentativesandcustomers.WithH2ODriverlessAI,datateamshaveeverythingtheyneedtobuildtrustwithbusinessstakeholdersandregulators.

OverviewofAutoMLandH2ODriverlessAI

H2ODriverlessAIdeliversenterprise-ready,scalable,andsecureAutoMLthatcanrunonanycloudplatformorinon-premisesenvironments,usingthearchitecturethatthisdocumentdescribes.Withanon-premisesenvironment,youdonotneedtomoveyourdatatothecloud;youcanperformAutoMLsecurelywhereveryourdataresides.

H2ODriverlessAIenablesdatascientiststoworkonprojectsfasterandmoreefficientlybyusingautomationtoperformkeymachinelearningtasksinminutesorhours,notmonths.

H2ODriverlessAIincreasestheproductivityofdatapractitionersbyautomatingdataprocessing,featureengineering,modelbuilding,andhyperparametertuning.Itisastand-aloneplatformthatcanbeappliedforusecasessuchasNaturalLanguageProcessing(NLP),timeseriesforecasting,andimageclassifications.EnterprisescanchoosetodeployanMLOPsplatformtoenablecross-functionalcollaborationandtomanagetheend-to-endlifecycleoftheirAIapplications.Inthosecases,userscanintegrateH2ODriverlessAIwiththeirMLOpsplatformsuchascnvrg.io(see

InvokingH2ODriverlessAI

fromcnvrg.ioMLOpsPlatform

).

AutoMLworkflowwith

ThefollowingfigureshowsthestepsinatypicalAutoMLworkflowandhowH2ODriverlessAIenablesthesesteps:

H2ODriverlessAI

Figure1. AutoMLworkflowinH2ODriverlessAI

OverviewofAutoMLandH2ODriverlessAI

OverviewofAutoMLandH2ODriverlessAI

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Dataingestion—Theworkflowbeginswiththedata.Dataingestionconsistsofimportingandobtainingdatatoperformanalysisandtraining.

H2ODriverlessAIcaningestdatafromdatasetsinvariousformatsandfilesystemsincludingHadoopHDFS,AmazonS3compatiblestorage,AzureBlobStorage,GoogleBigQuery,GoogleCloudStorage,ApacheHive,JDBC,kdb+,MinIO,Snowflake,DataRecipe,DataRecipeFile,andNFS.ForlargerdatasetsthatarealreadyavailableinPowerScalestorage,H2ODriverlessAIprovidesdataconnectorsforaccessingandingestingdata.

Datapreparation—Whenthedataisdefined,thenextstepisdatapreparation.Thedatasetcanbedividedintotraining,test,andvalidationdatasets.Datascientistscaninteractivelymodelthedataforexploration,analysis,andvisualizationusingdataplotsandstatistics.AutoMLtoolsautomaticallyperformfeatureengineeringbyextractingfeatures(domain-specificattributes)fromrawdataanddatatransformationstosuiteMLalgorithms.

H2ODriverlessAIdeterminesthebestpipelineforadataset,includingautomaticdatatransformationandfeatureengineering.Datascientistscancontrolthenumberoforiginalfeaturesusedinmodelbuildingbyselectingorexcludingcolumnsinthedataset.H2ODriverlessAIusesauniquegeneticalgorithmtoautomaticallyfindnew,high-valuefeaturesandfeaturecombinationsforaspecificdatasetthatarevirtuallyimpossibletofindmanually.Theinterfaceincludesaneasy-to-readvariableimportancechartthatshowsthesignificanceoforiginalandnewlyengineeredfeatures.

Automaticvisualizations(AutoViz)inH2ODriverlessAIproviderobustexploratorydataanalysiscapabilitiesbyautomaticallyselectingdataplotsbasedonthemostrelevantdatastatisticsthatarebasedonthedatashape.Inspecificcases,AutoVizcansuggeststatisticaltransformationforsomedata.Experienceduserscanalsocustomizevisualizationstomeettheirneeds.AutoVizhelpsusersdiscovertrendsandissuessuchaslargenumbersofmissingvaluesorsignificantoutliersthatcanimpactmodelingresults.

Modelbuilding—Whenthedataisprepared,thenextstepismodelbuilding.AutomaticmodelbuildingincludesdatatransformationsandhyperparametertuningforthevariousmodelsavailableintheAutoMLproduct.Itautomaticallytrainsseveralin-builtmodelsandselectsthebestmodelorafinalensembleofmodelsbasedonuser-definedparameterssuchasmodelaccuracy.

AutomaticmodeldevelopmentinH2ODriverlessAIisaccomplishedbyrunningexperiments.H2ODriverlessAItrainsmultiplemodelsandincorporatesmodelhyperparametertuning,scoring,andensembling.Datascientistscanconfigureparameterssuchastheaccuracy,time,lossfunction,andinterpretabilityforaspecificexperiment.Thispreviewisautomaticallyupdatedwhenanyoftheexperiment’ssettingschange(includingtheknobs).Userscanalsorunmultiplediverseexperimentsthatprovideanoverviewofthedataset.Thisfeatureprovidesdatascientistswithrelevantinformationfordeterminingcomplexity,accuracy,size,andtimetradeoffswhenputtingmodelsintoproduction.H2ODriverlessAIusesageneticalgorithmthatincorporatesa‘survivalofthefittest’concepttodeterminethebestmodelforspecificdatasetandconfiguredoptionsautomatically.

Productization—Whentheexperimentiscompleted,youcanmakenewpredictionsandpushthemodelforproduction,eitherinthecloud,on-premises,orattheedge.

H2ODriverlessAIoffersconvenientoptionsfordeployingmachinelearningmodels,dependingonwheretheAIapplicationisrun:

Downloadthemodelandbuildyourowncontainer.

Downloadascoringpipeline.

Whentheexperiment(modelbuildingstep)iscomplete,H2ODriverlessAIcanbuildascoringpipelinethatcanbedeployedtoproduction.Ascoringpipelineisapackagedexperimentwhichincludesartifactsnecessaryformodeldeployment,includingmodelbinary,runtime,readme,example,scripts,andsoon.Youcandownloadtwodifferenttypesofscoringpipelines:

PythonScoringPipeline

MOJOScoringPipeline,whichisavailablewithbothJavaandC++backends

Thedecisionaboutwhichtypeofpipelinetousecomesfromvariousfactorsincludingthetypeofmodelbeingbuiltintheexperiment,usecase,latencyrequirements,andsoon.Ingeneral,MOJOScoringPipelinesarefasterbutmightrequireadditionalsetup,whilePythonScoringPipelinesarebuiltintoa

.whlfile,whicheasilyinstallableinPython.H2ODriverlessAIalsoallowsyoutovisualizethescoringpipelineasadirectionalgraph,asshowninthefollowingfigure:

Figure2. VisualizationofH2ODriverlessAIscoringpipeline

Deploythemodeldirectlyinacloudservice.

ConfigurethemodeltorunonalocalRESTserverwithacoupleofclicks.

Keyfeatures

TheH2ODriverlessAIplatformenablesthefollowingelementsofAutoML:

SupportforNVIDIAGPUs—AImodelsareexplodingincomplexity,andautomateddatatransformationanddeeplearningrequiremassivecomputepowerandscalability.H2ODriverlessAIsupportsthelatestNVIDIAGPUstoacceleratefeatureengineeringandtrainingofneuralnetworks.NVIDIA’sMulti-InstanceGPU(MIG)featurecanbeusedtopartitiontheGPUs,increaseoverallGPUutilization,andsupportseveraltypesofusecasesanddeploymentswithguaranteedqualityofservice.

Integratedcatalogofrecipesandmodels—H2ODriverlessAIoffersarichcatalogofAImodels,transformers,andscorersforautomaticfeatureengineeringandmodelbuilding.

Machinelearninganddeeplearning—H2ODriverlessAIincludesleadingopen-sourcetransformers,embeddings,andframeworksformachinelearninganddeeplearningtechniquestohandlevariousdatascienceusecases.WithH2ODriverlessAI,youcanautomaticallybuildmodelsforIndependentandIdenticallyDistributed(IID)data,images,text,andmore.Forexample,H2ODriverlessAIincludesTensorFlowCNNsforimagemodelingandNLPlibrariesfromPyTorch,includingBERTandotherstate-of-the-arttechniques.

MachineLearningInterpretability(MLI)—H2ODriverlessAIprovidesrobustexplainabilityandfairnessanalysisformachinelearningmodelsandhelpsexploreanddemystifymodelingresults.Itincludesstraightforwarddisparateimpactanalysistotestformodelbiasandprovidesreasoncodesforeveryprediction.Maximumtransparencyandminimaldisparateimpactarecrucialdifferentiatorsifyoumustjustifyyourmodelstobusinessstakeholdersandregulators.

Automaticmodeldocumentation(AutoDoc)—Datascientistsmustdocumentthedata,algorithms,andprocessesusedtocreatemachinelearningmodelsforbusinessusersandregulators.H2ODriverlessAIautomaticmodeldocumentationrelievesyoufromthetime-consumingtaskofrecordingandsummarizingyourworkflowwhilebuildingmachinelearningmodels.Thedocumentationincludesdetailsaboutthedataused,thevalidationschemaselected,modelandfeaturetuning,MLI,andthefinalmodelcreated.AutoDocsavesdatascientiststimeandremovestediousworksothattheycanspendmoretimepracticingdatascienceanddrivemorevalueforthebusiness.

Bring-Your-OwnRecipes—ExperienceddatascientistscaneasilyextendH2ODriverlessAIwithcustomizationsthatrunwithintheH2ODriverlessAIplatform,includingdatapreparation,models,transformers,andscorers.Thesecustomizations,calledrecipes,arePythoncodesnippetsthatcanbeuploadedintoH2ODriverlessAIatruntime,likeplugins.H2ODriverlessAIcanconsumerecipeswithmultipleconvenientoptions:uploadingfromalocalmachine,consumingfrompublishedcodeinasourcecontrolhub(Bitbucket)andlinkingtoareciperawcode.YoucanchecktheGitHubrepositoryfortheavailableandoptimizedH2O.airecipes.Duringtrainingofasupervisedmachinelearningmodelingpipeline,H2ODriverlessAIcanusetheserecipesasbuildingblockswithorinsteadofallintegratedcodepieces.Theyareusedintheautomaticmachinelearningoptimizationprocess,eventuallycreatingthewinningmodel.Datascienceteamscandevelopcustomizationsspecifictotheiruse-cases,industry,orbusiness.

SolutionarchitectureforAutoML

SolutionarchitectureforAutoML

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SolutionarchitectureforAutoML

H2ODriverlessAIprovidesanenterprise-readyAutoMLproductfordatascientistsandmachinelearningengineerstodevelopandpublishAIapplications.ItcanbedeployedeitherinKubernetesaspodsorasastand-alonecontainer.

Kubernetes-baseddeploymentusingEnterpriseSteam

EnterpriseSteamfromH2O.aiisaserviceforsecurelymanaginganddeployinginfrastructureforH2ODriverlessAIonKubernetes.EnterpriseSteamofferssecurity,accesscontrol,resourcecontrol,andresourcemonitoringoutoftheboxsothatorganizationscanfocusonthecoreoftheirdatasciencepractice.Itenablessecure,streamlinedadoptionofH2ODriverlessAIandotherH2O.aiproductsthatcomplieswithcompanypolicies.

Fordatascientists,EnterpriseSteamprovidesPython,R,andwebclientsformanagingclustersandinstances.ItallowsdatascientiststopracticedatascienceintheirownH2ODriverlessAIinstance.Foradministrators,EnterpriseSteamcontrolswhichproductversionsandcomputeresourcesareavailable.

EnterprisesteamisasinglepodthatisdeployedusingHelm.WhenEnterpriseSteamisdeployed,youcanlaunchanewH2ODriverlessAIinstanceandmanageexistinginstances.

Youcanuseeachinstanceformodelbuildingforaspecificproject.Inthefollowingfigure,weshowthreeinstancesofH2ODriverlessAIdeployedforautomatedmodelbuildingforthreedifferentusecases:NLP,timeseriesforecasting,andimageclassification.

Figure3. SolutionarchitectureforKubernetes-basedDriverlessAIdeployment

Datasetsaremadeavailabletotheinstanceeitherbydownloadingthemintothecontainerorthroughseveralofthedataconnectors,asexplainedinthefollowingsections.Datavisualization,featureengineering,andmodeldevelopmentareperformed

onthisinstance.H2ODriverlessAIsupportsNVIDIAGPUaccelerationandsomeusecasessuchasimageclassificationcanbenefitfromGPUresources.Fortheseusecases,GPUsareconfiguredandmadeavailabletothecontainer.

Afterthemodelistrained,youcandownloadthePythonorMOJOScoringPipelineandbuildaDockercontainer.YoucandeploythisDockercontaineroutsideoftheKubernetesenvironmentoraspodexposedasaKubernetesservice.

H2ODriverlessAIcanalsobedeployedasastand-alonecontainereitheronbaremetalorvirtualmachines.Thisdeploymentoptionisoutsidethescopeofthisvalidateddesign.Seethe

H2ODriverlessAIdocumentation

formoreinformation.

Dockerimage

Security

GPUsupport

H2ODriverlessAIDockerimagesareavailablethroughEnterpriseSteam.TheDockerimagescomewithalltherequiredlibrariesandsoftwareinstalled,includinglibrariesfortheGPU.

EnterpriseSteamprovidesaccesscontrol.Userscanbecreatedwithdifferentroles,andresourcescanbeallocatedtoeachuser.H2ODriverlessAIsupportsclientcertificate,LDAP,andotherauthenticationoptions.TheseoptionscanbeconfiguredbyspecifyingtheenvironmentvariableswhenstartingtheH2ODriverlessAIDockerimageorbyspecifyingtheappropriateoptionsintheconfigurationfile.Seethe

H2ODriverlessAI

documentation

formoreinformation.

H2ODriverlessAIcanrunonmachineswithonlyCPUsormachineswithCPUsandGPUs.H2ODriverlessAIsupportsNVIDIAA100andA30GPUs.OnlyoneGPUissupportedperinstance.ImageandNLPusecasesinH2ODriverlessAIbenefitsignificantlyfromGPUusage.ModelbuildingalgorithmssuchasXGBoost(GBM/DART/RF/GLM),LightGBM(GBM/DART/RF),PyTorch(BERTmodels),andTensorFlow(CNN/BiGRU/ImageNet)modelsuseGPU.

NVIDIA’sMulti-InstanceGPU(MIG)featurecanbeusedtopartitiontheGPUs,increaseoverallGPUutilization,andsupportseveraltypesofusecasesanddeploymentswithguaranteedqualityofservice.FormoreinformationaboutGPUpartitioningrecommendations,seetothe

NVIDIAMulti-InstanceGPUandNVIDIATechnicalBrief.

ImageandNLPusecasesinH2ODriverlessAIbenefitsignificantlyfromGPUusage.ModelbuildingalgorithmssuchasXGBoost(GBM/DART/RF/GLM),LightGBM(GBM/DART/RF),PyTorch(BERTmodels),andTensorFlow(CNN/BiGRU/ImageNet)modelsuseGPU.

InvokingH2ODriverlessAIfromcnvrg.ioMLOpsPlatform

InvokingH2ODriverlessAIfromcnvrg.ioMLOpsPlatform

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Storageandnetworkconfiguration

Licensing

H2ODriverlessAIrequiresnospecialnetworkconsiderations.TheKubernetes-baseddeploymentusesingresscontrolandloadbalancerstogovernaccesstothedeployment.

H2ODriverlessAIusespersistentvolumestosavetherequireddataandtoconnecttoexternaldatasourcessuchasNFS.

H2ODriverlessAIislicensedperuser.EachusercandeployaninstanceofH2ODriverlessAI.H2ODriverlessAImanagestheGPUsinthedeployment.Itensuresthatdifferentexperimentsbydifferentuserscanrunsafelysimultaneouslyanddonotinterferewitheachother.NospeciallicensingisrequiredforGPUsupport.

EnterpriseSteamislicensedseparately.UsersrequireonelicenseperEnterpriseSteamdeployment.

InvokingH2ODriverlessAIfromcnvrg.ioMLOpsPlatform

Asshownin

Figure1,

AutoMLenablesautomaticmodelbuilding.However,itdoesnotofferthecompletelifecycleforamachinelearningapplication.Also,AutoMLautomatedmodelbuildingdoesnotsupportallscenariosandusecases.Forexample,AutoMLsupportstrainingonlyforsuperviseddataandunsupervisedlearning.Itdoesnotsupportreinforcementlearning.

ForbuildingmodelsforsuchcomplexusecasesandtomaintainacompletelifecycleofAImodels,enterprisesrelyonanMLOPsplatform.MLOpsisadefinedprocessandlifecycleformachinelearningdata,models,andcoding.TheMLOpslifecyclebeginswithdataextractionandpreparationasthedatasetismassagedintoastructurethatcaneffectivelyfeedthemodel.MLOpsplatformsprovideconstantmonitoringtoensurethattheprocessisrunningsmoothly.MLOpsenablesdatascientiststobuildcomplexpipelinesthatallowforcontinuouslearning.Automaticretrainingcanbeimplementedtohelpadjustthedeployedprocessandimprovetheaccuracywitheachiteration.

EnterprisesthathavemultipleongoingAIprojectstosupportprogresstowardstheirbusinessintelligencegoalscanusebothMLOpsandAutoMLplatformstotheirrespectivestrengths.DellTechnologieshasworkedcloselywithcnvrg.iotodeliverMLOpsforAIandmachinelearningadoptersthroughajointlyengineeredandtestedsolutiontohelporganizationscapitalizeonthebenefitsofMLOpsformachinelearningandAIworkloads.TheOptimizeMachineLearningThroughMLOpswithDellTechnologiesandcnvrg.io

WhitePaper

and

DesignGuide

provideguidanceforarchitecting,deploying,andoperatingMLOps