| Name: | Description: | Size: | Format: | |
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| DM_FilipeOliveira_MEI_2023 | 4.71 MB | Adobe PDF |
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Abstract(s)
Machine Learning (ML) eInteligência Artificial(IA )são dois termos intimamente relacionados. A Inteligência Artificial é uma disciplina que busca criar máquinas que tenham a capacidade de imitar as habilidades cognitivas humanas, como aprendizagem, raciocínio, perceção, e tomada de decisão. Machine Learning é uma das técnicas de IA que permite às máquinas aprenderem a partir de dados sem serem explicitamente programa das. O crescimento exponencial dos dados nas últimas décadas tem sido um dos principais fatores impulsionadores do avanço da Inteligência Artificial e de MachineLearning. As empresas e organizações recolhem dados em volumes cada vez maiores, incluindo informações de transações financeiras, registos médicos, dados de sensoresIoTemuitomais.Essesdadossãocruciaisparaimpulsionarainovaçãoeo progresso, mas podem ser muito complexos e difíceis de ser em analisados manualmente. É aqui que entram MachineLearning, que permite que as máquinas aprendam e automatizem a análise de grandes conjuntos de dados.
Machine Learning(ML)andArtificialIntelligence(AI)aretwocloselyrelatedterms.ArtificialIn- telligence isadisciplinethatseekstocreatemachinesthathavetheabilitytomimichumancognitive skills, suchaslearning,reasoning,perception,anddecisionmaking.MachineLearningisoneoftheAI techniques thatallowsmachinestolearnfromdatawithoutbeingexplicitlyprogrammed. The exponentialgrowthofdatainrecentdecadeshasbeenoneofthemaindrivingfactorsofAIand Machine Learningadvancement.Companiesandorganizationscollectdatainincreasinglylargevolumes, including financialtransactioninformation,medicalrecords,IoTsensordata,andmore.Thisdatais crucial fordrivinginnovationandprogress,butcanbetoocomplexanddifficulttoanalyzemanually. This iswhereMachineLearningcomesin,allowingmachinestolearnandautomatetheanalysisof largedatasets.Thisapproachreducesthetimeandeffortrequiredtoperformcomplexanalyses,aswell as providingvaluableinsightsthatcanbeusedtoimprovebusinessoperations,increaseefficiency,and makemoreinformeddecisions. As datacontinuestogrowinsizeandcomplexity,newapproachesandsystemsareneededto handle itefficiently.OnewaythisisbeingdoneisthroughthedevelopmentofmoreadvancedMachine Learning techniques,suchasdeepneuralnetworksandreinforcementlearningalgorithms,whichcan more effectivelyhandlelargerandmorecomplexdatasets.Inaddition,theuseoftechnologiessuchas cloud computinganddistributeddataprocessingcanalsohelpreducetheconsumptionofcomputational resources andmakedataanalysismorescalable. Thus, theproposedsolutionarisestoaddresssomeofthechallengesthathaveemergedwiththe increase indatavolume.AdistributedmachinelearningsystemthatrunsonaHadoopclusterand takesadvantageofreplication,balancing,andblockdistributioncapabilities.Itallowsmodelstobe trained inadistributedmannerfollowingtheprincipleofdatalocality,beingabletochangepartsof the modelthroughanoptimizationmodule,thusenablingthemodeltoevolveovertimeasnewdataarrives
Machine Learning(ML)andArtificialIntelligence(AI)aretwocloselyrelatedterms.ArtificialIn- telligence isadisciplinethatseekstocreatemachinesthathavetheabilitytomimichumancognitive skills, suchaslearning,reasoning,perception,anddecisionmaking.MachineLearningisoneoftheAI techniques thatallowsmachinestolearnfromdatawithoutbeingexplicitlyprogrammed. The exponentialgrowthofdatainrecentdecadeshasbeenoneofthemaindrivingfactorsofAIand Machine Learningadvancement.Companiesandorganizationscollectdatainincreasinglylargevolumes, including financialtransactioninformation,medicalrecords,IoTsensordata,andmore.Thisdatais crucial fordrivinginnovationandprogress,butcanbetoocomplexanddifficulttoanalyzemanually. This iswhereMachineLearningcomesin,allowingmachinestolearnandautomatetheanalysisof largedatasets.Thisapproachreducesthetimeandeffortrequiredtoperformcomplexanalyses,aswell as providingvaluableinsightsthatcanbeusedtoimprovebusinessoperations,increaseefficiency,and makemoreinformeddecisions. As datacontinuestogrowinsizeandcomplexity,newapproachesandsystemsareneededto handle itefficiently.OnewaythisisbeingdoneisthroughthedevelopmentofmoreadvancedMachine Learning techniques,suchasdeepneuralnetworksandreinforcementlearningalgorithms,whichcan more effectivelyhandlelargerandmorecomplexdatasets.Inaddition,theuseoftechnologiessuchas cloud computinganddistributeddataprocessingcanalsohelpreducetheconsumptionofcomputational resources andmakedataanalysismorescalable. Thus, theproposedsolutionarisestoaddresssomeofthechallengesthathaveemergedwiththe increase indatavolume.AdistributedmachinelearningsystemthatrunsonaHadoopclusterand takesadvantageofreplication,balancing,andblockdistributioncapabilities.Itallowsmodelstobe trained inadistributedmannerfollowingtheprincipleofdatalocality,beingabletochangepartsof the modelthroughanoptimizationmodule,thusenablingthemodeltoevolveovertimeasnewdataarrives
Description
Keywords
Distributed Machine Learning Hadoop Distributed File System