{"id":86523,"date":"2020-06-26T07:42:14","date_gmt":"2020-06-26T05:42:14","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/mlops-devops-v-mire-machine-learning"},"modified":"2020-06-26T07:42:14","modified_gmt":"2020-06-26T05:42:14","slug":"mlops-devops-v-mire-machine-learning","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sq\/blog\/administrirovanie\/mlops-devops-v-mire-machine-learning","title":{"rendered":"MLOps: DevOps n\u00eb bot\u00ebn e Machine Learning","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>N\u00eb vitin 2018, n\u00eb qarqet profesionale dhe n\u00eb konferencat tematike t\u00eb dedikuara AI, u shfaq koncepti i MLOps, i cili u p\u00ebrqafua shpejt n\u00eb industrin\u00eb dhe tani po zhvillohet si nj\u00eb drejtim i pavarur. N\u00eb perspektiv\u00eb, MLOps mund t\u00eb b\u00ebhet nj\u00eb nga fushat m\u00eb t\u00eb k\u00ebrkuara n\u00eb IT. \u00c7far\u00eb \u00ebsht\u00eb kjo dhe si e p\u00ebrdorim, e shqyrtojm\u00eb m\u00eb posht\u00eb.<\/p>\n<p><img decoding=\"async\" alt=\"MLOps: DevOps n\u00eb bot\u00ebn e Machine Learning\" src=\"\/wp-content\/uploads\/2020\/06\/03bcaaaf5b9ddd864c7e8e0a607d81c4.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>\u00c7far\u00eb \u00ebsht\u00eb MLOps<\/h2>\n<p>\nMLOps (bashkimi i teknologjive dhe proceseve t\u00eb m\u00ebsimit makin\u00eb dhe qasjeve p\u00ebr integrimin e modeleve t\u00eb zhvilluara n\u00eb proceset biznesore) \u00ebsht\u00eb nj\u00eb m\u00ebnyr\u00eb e re bashk\u00ebpunimi midis p\u00ebrfaq\u00ebsuesve t\u00eb biznesit, shkenc\u00ebtar\u00ebve, matematicien\u00ebve, specialist\u00ebve t\u00eb m\u00ebsimit makin\u00eb dhe inxhinier\u00ebve IT p\u00ebr krijimin e sistemeve t\u00eb inteligjenc\u00ebs artificiale.<\/p>\n<p>N\u00eb fjal\u00eb t\u00eb tjera, kjo \u00ebsht\u00eb nj\u00eb m\u00ebnyr\u00eb p\u00ebr t\u00eb kthyer metodat dhe teknologjit\u00eb e m\u00ebsimit makin\u00eb n\u00eb nj\u00eb mjet t\u00eb dobish\u00ebm p\u00ebr zgjidhjen e problemeve biznesore.\u00a0<\/p>\n<p>Duhet t\u00eb kuptojm\u00eb se zinxhiri i produktivizimit fillon shum\u00eb p\u00ebrpara zhvillimit t\u00eb modelit. Hapi i par\u00eb \u00ebsht\u00eb p\u00ebrcaktimi i gjith\u00eb k\u00ebrkes\u00ebs s\u00eb biznesit, hipotezat p\u00ebr vler\u00ebn q\u00eb mund t\u00eb nxirret nga t\u00eb dh\u00ebnat dhe ideja e biznesit p\u00ebr aplikimin e saj.\u00a0<\/p>\n<p>Vet\u00eb koncepti i MLOps u shfaq si nj\u00eb analogji me konceptin DevOps n\u00eb lidhje me modelet dhe teknologjit\u00eb e m\u00ebsimit makin\u00eb. DevOps \u00ebsht\u00eb nj\u00eb qasje p\u00ebr zhvillimin e softuerit, q\u00eb lejon rritjen e shpejt\u00ebsis\u00eb s\u00eb implementimit t\u00eb ndryshimeve t\u00eb ve\u00e7anta duke mbajtur fleksibilitetin dhe besueshm\u00ebrin\u00eb me an\u00eb t\u00eb disa qasjeve, mes t\u00eb cilave zhvillimi t\u00eb vazhduar, ndarja e funksioneve n\u00eb nj\u00eb s\u00ebr\u00eb mikro-sh\u00ebrbimesh t\u00eb pavarura, testim t\u00eb automatizuar dhe implementim t\u00eb ndryshimeve t\u00eb ve\u00e7anta, monitorim global t\u00eb funksionimit, sistem p\u00ebr reagimin e shpejt\u00eb ndaj d\u00ebshtimeve t\u00eb identifikuara, etj.\u00a0<\/p>\n<p>DevOps p\u00ebrcaktoi ciklin e jet\u00ebs s\u00eb softuerit dhe n\u00eb komunitetin e specialist\u00ebve erdhi ideja q\u00eb t\u00eb p\u00ebrdoret e nj\u00ebjta metodologji n\u00eb lidhje me t\u00eb dh\u00ebnat e m\u00ebdha. DataOps \u00ebsht\u00eb p\u00ebrpjekja p\u00ebr t\u00eb p\u00ebrshtatur dhe zgjeruar metodologjin\u00eb, duke pasur parasysh ve\u00e7antit\u00eb e ruajtjes, transferimit dhe p\u00ebrpunimit t\u00eb masave t\u00eb m\u00ebdha t\u00eb t\u00eb dh\u00ebnave n\u00eb platforma t\u00eb ndryshme dhe q\u00eb nd\u00ebrveprojn\u00eb me nj\u00ebra-tjetr\u00ebn.<br \/>\n\u00a0\u00a0<br \/>\nMe shfaqjen e nj\u00eb mase kritike t\u00eb modeleve t\u00eb m\u00ebsimit t\u00eb makineris\u00eb, t\u00eb implementuara n\u00eb proceset e biznesit t\u00eb nd\u00ebrmarrjeve, \u00ebsht\u00eb v\u00ebn\u00eb re nj\u00eb ngjashm\u00ebri e fort\u00eb midis ciklit t\u00eb jet\u00ebs s\u00eb modeleve matematikore t\u00eb m\u00ebsimit t\u00eb makineris\u00eb dhe ciklit t\u00eb jet\u00ebs s\u00eb softuerit. Diferenca \u00ebsht\u00eb vet\u00ebm se algoritmet e modeleve krijohen me an\u00eb t\u00eb mjeteve dhe metodave t\u00eb m\u00ebsimit t\u00eb makineris\u00eb. Prandaj, natyrsh\u00ebm lindi ideja p\u00ebr t\u00eb aplikuar dhe adaptuar p\u00ebr modelet e m\u00ebsimit t\u00eb makineris\u00eb qasjet e njohura p\u00ebr zhvillimin e softuerit. K\u00ebshtu, n\u00eb ciklin e jet\u00ebs s\u00eb modeleve t\u00eb m\u00ebsimit t\u00eb makineris\u00eb mund t\u00eb ve\u00e7ohen k\u00ebto etapa ky\u00e7e:<\/p>\n<ul>\n<li>definimi i ideve t\u00eb biznesit;\n<\/li>\n<li>trajnim i modelit;\n<\/li>\n<li>testimi dhe implementimi i modelit n\u00eb procesin e biznesit;\n<\/li>\n<li>shfryt\u00ebzimi i modelit.\n<\/li>\n<\/ul>\n<p>\nKur gjat\u00eb shfryt\u00ebzimit lind nevoja p\u00ebr t\u00eb ndryshuar ose p\u00ebr t\u00eb trajnuar s\u00ebrish modelin me t\u00eb dh\u00ebna t\u00eb reja, cikli rinis \u2014 modeli ripunoh\u00ebt, testohet, dhe nj\u00eb version i ri shp\u00ebrndahet.<\/p>\n<blockquote><p>Shk\u00ebputje. Pse rip\u00ebrfshirje dhe jo rip\u00ebrshkrim? Termi \"rip\u00ebrfshirje e modelit\" ka kuptime t\u00eb dyfishta: mes specialist\u00ebve ai n\u00ebnkupton nj\u00eb defekt t\u00eb modelit, kur modeli parashikon mir\u00eb, faktikisht p\u00ebrs\u00ebrit parametrin e parashikuar n\u00eb mostr\u00ebn e trajnuar, por punon shum\u00eb m\u00eb keq n\u00eb mostr\u00ebn e dh\u00ebnave t\u00eb jashtme. Natyrsh\u00ebm, nj\u00eb model i till\u00eb \u00ebsht\u00eb defekt, pasi ky problem nuk e lejon at\u00eb t\u00eb p\u00ebrdoret.<\/p><\/blockquote>\n<p>\nN\u00eb k\u00ebt\u00eb cik\u00ebl t\u00eb jet\u00ebs, duket e arsyeshme t\u00eb p\u00ebrdoren mjetet e DevOps: testimi automatizuar, shp\u00ebrndarja dhe monitorimi, form\u00ebsimi i llogaritjeve t\u00eb modeleve n\u00eb form\u00ebn e mikrosh\u00ebrbimeve t\u00eb ve\u00e7anta. Por ka edhe disa ve\u00e7ori q\u00eb pengojn\u00eb p\u00ebrdorimin e drejtp\u00ebrdrejt\u00eb t\u00eb k\u00ebtyre mjeteve pa nj\u00eb mb\u00ebshtetje shtes\u00eb t\u00eb ML.<\/p>\n<p><img decoding=\"async\" alt=\"MLOps: DevOps n\u00eb bot\u00ebn e Machine Learning\" src=\"\/wp-content\/uploads\/2020\/06\/e1d01fd68c3f0071f925a82c08d29d25.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h2>Si t\u00eb b\u00ebjm\u00eb q\u00eb modelet t\u00eb punojn\u00eb dhe t\u00eb sjellin fitim<\/h2>\n<p>\nSi e mori q\u00eb ne do ta ilustrojm\u00eb p\u00ebrdorimin e qasjes MLOps, do t\u00eb marrim detyr\u00ebn e klas\u00ebs klasike p\u00ebr robotizimin e bisedave mb\u00ebshtet\u00ebse t\u00eb produkteve bankare (ose \u00e7do produkti tjet\u00ebr). N\u00eb p\u00ebrgjith\u00ebsi, procesi i mb\u00ebshtetjes me an\u00eb t\u00eb bisedave duket si m\u00eb posht\u00eb: klienti shkruan nj\u00eb mesazh me nj\u00eb pyetje n\u00eb bised\u00eb dhe merr nj\u00eb p\u00ebrgjigje nga nj\u00eb specialist brenda nj\u00eb strukture t\u00eb paracaktuar dialogu. Detyra e automatizimit t\u00eb k\u00ebtij bisedimi zakonisht zgjidhet me an\u00eb t\u00eb grupeve t\u00eb rregullave t\u00eb p\u00ebrcaktuara nga ekspert\u00eb, t\u00eb cilat jan\u00eb shum\u00eb t\u00eb lodhshme p\u00ebr t\u00eb zhvilluar dhe mbajtur. Efektiviteti i k\u00ebsaj automatizimi, n\u00eb var\u00ebsi t\u00eb nivelit t\u00eb v\u00ebshtir\u00ebsis\u00eb s\u00eb detyr\u00ebs, mund t\u00eb arrij\u00eb 20\u201330%. Natyrisht, lind ideja se \u00ebsht\u00eb m\u00eb e leverdishme t\u00eb implementohet nj\u00eb moduli inteligjenc\u00ebs artificiale \u2014 nj\u00eb model i dizenjuar p\u00ebrmes m\u00ebsimit t\u00eb makinerive, i cili:<\/p>\n<ul>\n<li>\u00ebsht\u00eb n\u00eb gjendje t\u00eb p\u00ebrpunoj\u00eb nj\u00eb num\u00ebr m\u00eb t\u00eb madh k\u00ebrkesash pa ndihm\u00ebn e operatorit (n\u00eb var\u00ebsi t\u00eb tem\u00ebs, n\u00eb disa raste efektiviteti mund t\u00eb arrij\u00eb deri n\u00eb 70\u201380%);\n<\/li>\n<li>adaptosh m\u00eb mir\u00eb n\u00eb formulime jo standarde n\u00eb bised\u00eb \u2014 di t\u00eb p\u00ebrcaktoj\u00eb intenc\u00ebn, d\u00ebshir\u00ebn reale t\u00eb p\u00ebrdoruesit p\u00ebr nj\u00eb k\u00ebrkes\u00eb q\u00eb nuk \u00ebsht\u00eb formuluar qart\u00eb;\n<\/li>\n<li>di t\u00eb p\u00ebrcaktoj\u00eb se kur p\u00ebrgjigjja e modelit \u00ebsht\u00eb adekuate dhe kur ndodhen dyshime n\u00eb lidhje me 'nd\u00ebrgjegjshm\u00ebrin\u00eb' e k\u00ebsaj p\u00ebrgjigjeje dhe duhet t\u00eb b\u00ebhet nj\u00eb pyetje e sqarimit ose t\u00eb kaloj\u00eb te operatori;\n<\/li>\n<li>mund t\u00eb rishkruhet automatikisht (n\u00eb vend t\u00eb nj\u00eb grupi zhvilluesish q\u00eb vazhdimisht adaptojn\u00eb dhe korrigjojn\u00eb skenar\u00ebt e p\u00ebrgjigjeve, modeli rishkruhet nga nj\u00eb specialist i Shkenc\u00ebs s\u00eb t\u00eb Dh\u00ebnave duke aplikuar bibliotekat p\u00ebrkat\u00ebse t\u00eb m\u00ebsimit t\u00eb makinerive).\u00a0\n<\/li>\n<\/ul>\n<p>\n<img decoding=\"async\" alt=\"MLOps: DevOps n\u00eb bot\u00ebn e Machine Learning\" src=\"\/wp-content\/uploads\/2020\/06\/9ef6356090a250df6cb212ab8bedb5f3.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nSi ta b\u00ebjm\u00eb t\u00eb funksionoj\u00eb nj\u00eb model t\u00eb till\u00eb t\u00eb avancuar?\u00a0<\/p>\n<p>Ashtu si me zgjidhjen e \u00e7do detyre tjet\u00ebr, p\u00ebrpara se t\u00eb zhvillojm\u00eb nj\u00eb modul t\u00eb till\u00eb, \u00ebsht\u00eb e nevojshme t\u00eb p\u00ebrcaktohet procesi i biznesit dhe t\u00eb p\u00ebrshkruhet formalisht detyra specifike q\u00eb do t\u00eb zgjidhim duke p\u00ebrdorur metod\u00ebn e m\u00ebsimit t\u00eb makinerive. N\u00eb k\u00ebt\u00eb pik\u00eb fillon procesi i operacionalizimit, i cili shenjohen n\u00eb akronimin Ops.\u00a0<\/p>\n<p>Hapi tjet\u00ebr, specialisti i Data Science n\u00eb bashk\u00ebpunim me inxhinierin e t\u00eb dh\u00ebnave kontrollon disponueshm\u00ebrin\u00eb dhe mjaftueshm\u00ebrin\u00eb e t\u00eb dh\u00ebnave dhe hipotez\u00ebs s\u00eb biznesit p\u00ebr funksionalitetin e ides\u00eb biznesore, duke zhvilluar nj\u00eb prototip t\u00eb modelit dhe duke e testuar efektivitetin e tij t\u00eb v\u00ebrtet\u00eb. Vet\u00ebm pasi q\u00eb biznesi ta konfirmoj\u00eb k\u00ebt\u00eb, mund t\u00eb filloj\u00eb kalimi nga zhvillimi i modelit n\u00eb integrimin e tij n\u00eb sisteme q\u00eb realizojn\u00eb nj\u00eb proces konkret biznesi. Planifikimi i gjithansh\u00ebm i zbatimit, kuptimi i thell\u00eb n\u00eb \u00e7do etap\u00eb se si do t\u00eb p\u00ebrdoret modeli dhe \u00e7far\u00eb ndikimi ekonomik do t\u00eb sjell\u00eb, \u00ebsht\u00eb nj\u00eb moment thelb\u00ebsor n\u00eb proceset e integrimit t\u00eb qasjeve MLOps n\u00eb peizazhin teknologjik t\u00eb kompanis\u00eb.<\/p>\n<p>Me zhvillimin e teknologjive t\u00eb Inteligjenc\u00ebs Artificiale, numri dhe diversiteti i detyrave q\u00eb mund t\u00eb zgjidhen p\u00ebrmes m\u00ebsimit t\u00eb makinerive rritet me nj\u00eb rit\u00ebm t\u00eb shpejt\u00eb. \u00c7do proces biznesi i till\u00eb \u00ebsht\u00eb nj\u00eb kursim p\u00ebr kompanin\u00eb p\u00ebr shkak t\u00eb automatizimit t\u00eb pun\u00ebs s\u00eb stafit t\u00eb pozita masive (qendra e thirrjeve, verifikimi dhe renditja e dokumenteve etj.), kjo \u00ebsht\u00eb zgjerimi i baz\u00ebs s\u00eb klient\u00ebve p\u00ebrmes shtimit t\u00eb funksioneve t\u00eb reja t\u00ebrheq\u00ebse dhe t\u00eb p\u00ebrshtatshme, kursimi i fondeve p\u00ebr shkak t\u00eb p\u00ebrdorimit optimal t\u00eb tyre dhe ribashkimit t\u00eb burimeve dhe shum\u00eb m\u00eb tep\u00ebr. N\u00eb fund t\u00eb fundit, \u00e7do proces \u00ebsht\u00eb i orientuar drejt krijimit t\u00eb vler\u00ebs dhe, si pasoj\u00eb, duhet t\u00eb sjell\u00eb nj\u00eb efekt ekonomik t\u00eb caktuar. K\u00ebtu \u00ebsht\u00eb shum\u00eb e r\u00ebnd\u00ebsishme t\u00eb formulosh qart\u00eb iden\u00eb e biznesit dhe t\u00eb llogaris\u00ebsh fitimin e pritur nga implementimi i modelit n\u00eb strukturen e p\u00ebrgjithshme t\u00eb krijimit t\u00eb vler\u00ebs p\u00ebr kompanin\u00eb. Ka raste kur implementimi i modelit nuk justifikon vetveten, dhe koha e shpenzuar nga specialist\u00ebt e m\u00ebsimit t\u00eb makinave e kalon q\u00ebndrim shum\u00eb m\u00eb t\u00eb shtrenjt\u00eb se sa vendi i pun\u00ebs i operatorit q\u00eb realizon k\u00ebt\u00eb detyr\u00eb. Prandaj, k\u00ebto raste duhet t\u00eb identifikohen sa m\u00eb her\u00ebt gjat\u00eb krijimit t\u00eb sistemeve t\u00eb Inteligjenc\u00ebs Artificiale.<\/p>\n<p>Prandaj, fitimi nga modeli fillon t\u00eb sjell\u00eb p\u00ebrfitime vet\u00ebm kur n\u00eb procesin e MLOps \u00ebsht\u00eb formuluar sakt\u00eb detyra e biznesit, jan\u00eb vendosur prioritetet dhe n\u00eb fazat fillestare t\u00eb zhvillimit \u00ebsht\u00eb formuluar procesi i integrimit t\u00eb modelit n\u00eb sistem.<\/p>\n<h2>Procesi i ri \u2013 sfida t\u00eb reja<\/h2>\n<p>\nNj\u00eb p\u00ebrgjigje e plot\u00eb mbi pyetjen kryesore t\u00eb biznesit se sa t\u00eb aplikueshme jan\u00eb modelet ML p\u00ebr zgjidhjen e problemeve, pyetja e p\u00ebrgjithshme e besimit n\u00eb AI \u00ebsht\u00eb nj\u00eb nga sfidat ky\u00e7e n\u00eb procesin e zhvillimit dhe implementimit t\u00eb qasjeve MLOps. Fillimisht, biznesi e sheh me skepticiz\u00ebm implementimin e m\u00ebsimit t\u00eb makinerive n\u00eb procese \u2014 \u00ebsht\u00eb e v\u00ebshtir\u00eb t\u00eb mb\u00ebshtetesh n\u00eb modele n\u00eb ato vende ku m\u00eb par\u00eb, zakonisht, punonin njer\u00ebzit. P\u00ebr biznesin, programet paraqiten si nj\u00eb \"kut i zi\", relevanca e p\u00ebrgjigjeve t\u00eb t\u00eb cilave ende duhet t\u00eb provohet. P\u00ebr m\u00eb tep\u00ebr, n\u00eb veprimtarin\u00eb bankare, n\u00eb biznesin e operator\u00ebve t\u00eb komunikimit dhe n\u00eb t\u00eb tjera, ekzistojn\u00eb k\u00ebrkesa t\u00eb rrepta nga rregullator\u00ebt shtet\u00ebror\u00eb. T\u00eb gjitha sistemet dhe algoritmet q\u00eb jan\u00eb implementuar n\u00eb proceset bankare iu n\u00ebnshtrohen auditeve. P\u00ebr t\u00eb zgjidhur k\u00ebt\u00eb problem, p\u00ebr t\u00eb provuar biznesit dhe rregullator\u00ebve arsyeshm\u00ebrin\u00eb dhe sakt\u00ebsin\u00eb e p\u00ebrgjigjeve t\u00eb inteligjenc\u00ebs artificiale, s\u00eb bashku me modelin implementohen mjete monitorimi. P\u00ebr m\u00eb tep\u00ebr, ekziston nj\u00eb procedur\u00eb e vlefshm\u00ebris\u00eb s\u00eb pavarur, e cila \u00ebsht\u00eb e detyrueshme p\u00ebr modelet rregullatore dhe p\u00ebrputhet me k\u00ebrkesat e Bank\u00ebs Qendrore. Nj\u00eb grup ekspert\u00ebsh t\u00eb pavarur kryen auditimin e rezultateve t\u00eb marra nga modeli duke marr\u00eb parasysh t\u00eb dh\u00ebnat hyr\u00ebse.<\/p>\n<p>Sfida e dyt\u00eb \u00ebsht\u00eb vler\u00ebsimi dhe llogaritja e riskut t\u00eb modeleve gjat\u00eb implementimit t\u00eb modelit t\u00eb m\u00ebsimit t\u00eb makinerive. N\u00ebse edhe njeriu nuk mund t\u00eb p\u00ebrgjigjet me nj\u00eb siguri t\u00eb nj\u00ebqind p\u00ebr qind n\u00eb pyetje, n\u00ebse ajo fustani ishte i bardh\u00eb apo blu, at\u00ebher\u00eb edhe inteligjenca artificiale ka t\u00eb drejt\u00eb t\u00eb b\u00ebj\u00eb gabime. Po ashtu, duhet marr\u00eb parasysh se me kalimin e koh\u00ebs t\u00eb dh\u00ebnat mund t\u00eb ndryshojn\u00eb dhe modelet duhet t\u00eb ri-trajnohen p\u00ebr t\u00eb dh\u00ebn\u00eb rezultate sa m\u00eb t\u00eb sakta. P\u00ebr t\u00eb mos d\u00ebmtuar procesin e biznesit, \u00ebsht\u00eb e nevojshme t\u00eb menaxhohen risqet e modeleve dhe t\u00eb monitorohet puna e modelit, duke e ri-trajnuar rregullisht at\u00eb me t\u00eb dh\u00ebna t\u00eb reja.<\/p>\n<p><img decoding=\"async\" alt=\"MLOps: DevOps n\u00eb bot\u00ebn e Machine Learning\" src=\"\/wp-content\/uploads\/2020\/06\/cb97ff0eab95d81e17bf76aad3c3994e.jpeg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nPor pas faz\u00ebs fillestare t\u00eb mosbesimit fillon t\u00eb shihet efekti i kund\u00ebrt. Sa m\u00eb shum\u00eb modele t\u00eb implementuara me sukses n\u00eb procese, aq m\u00eb shum\u00eb rritet apetiti i biznesit p\u00ebr t\u00eb p\u00ebrdorur inteligjenc\u00ebn artificiale \u2014 gjenden gjithnj\u00eb e m\u00eb shum\u00eb detyra q\u00eb mund t\u00eb zgjidhen p\u00ebrmes metodave t\u00eb m\u00ebsimit t\u00eb makinerive. \u00c7do detyr\u00eb nisi nj\u00eb proces t\u00eb t\u00ebr\u00eb q\u00eb k\u00ebrkon aft\u00ebsi t\u00eb ndryshme:<\/p>\n<ul>\n<li>inxhiner\u00ebt e t\u00eb dh\u00ebnave p\u00ebrgatitin dhe p\u00ebrpunojn\u00eb t\u00eb dh\u00ebnat;\n<\/li>\n<li>shkenc\u00ebtar\u00ebt e t\u00eb dh\u00ebnave p\u00ebrdorin mjetet e m\u00ebsimit t\u00eb makinerive dhe zhvillojn\u00eb modelin;\n<\/li>\n<li>IT integret modelin n\u00eb sistem;\n<\/li>\n<li>Inxhinieri i ML p\u00ebrcakton se si t\u00eb integrohet sakt\u00ebsisht ky model n\u00eb proces, cilat mjete IT duhet t\u00eb p\u00ebrdoren n\u00eb p\u00ebrputhje me k\u00ebrkesat p\u00ebr m\u00ebnyr\u00ebn e p\u00ebrdorimit t\u00eb modelit duke marr\u00eb parasysh fluksin e k\u00ebrkesave, koh\u00ebn e p\u00ebrgjigjes etj.\u00a0\n<\/li>\n<li>Arkitekt i ML projekton se si mund t\u00eb realizohet fizikisht produkte software n\u00eb sistemin industrial.\n<\/li>\n<\/ul>\n<p>\nCikli i t\u00ebr\u00eb k\u00ebrkon nj\u00eb num\u00ebr t\u00eb madh specialist\u00ebsh t\u00eb kualifikuar. N\u00eb nj\u00eb moment t\u00eb caktuar t\u00eb zhvillimit dhe shkall\u00ebs s\u00eb dep\u00ebrtimit t\u00eb modeleve ML n\u00eb proceset e biznesit, rezulton se t\u00eb rrit\u00ebsh n\u00eb m\u00ebnyr\u00eb lineare numrin e specialist\u00ebve n\u00eb p\u00ebrputhje me rritjen e numrit t\u00eb detyrave b\u00ebhet e shtrenjt\u00eb dhe e pap\u00ebrballshem. Prandaj, lind pyetja e automatizimit t\u00eb procesit MLOps - p\u00ebrcaktimi i disa klasave standarde t\u00eb detyrave t\u00eb m\u00ebsimit t\u00eb makin\u00ebs, zhvillimi i pipelineve tipike p\u00ebr p\u00ebrpunimin e t\u00eb dh\u00ebnave dhe p\u00ebrmir\u00ebsimin e modeleve. N\u00eb pamjen ideale p\u00ebr t\u00eb zgjidhur k\u00ebto detyra k\u00ebrkohen profesionist\u00eb q\u00eb zot\u00ebrojn\u00eb n\u00eb m\u00ebnyr\u00eb t\u00eb barabart\u00eb kompetencat n\u00eb nd\u00ebrveprimin e Big Data, Shkenc\u00ebs s\u00eb T\u00eb Dh\u00ebnave, DevOps dhe IT. Prandaj, problemi m\u00eb i madh n\u00eb industrin\u00eb e Shkenc\u00ebs s\u00eb T\u00eb Dh\u00ebnave dhe sfida m\u00eb e madhe n\u00eb organizimin e proceseve MLOps \u00ebsht\u00eb mungesa e k\u00ebsaj kompetence n\u00eb tregun aktual t\u00eb p\u00ebrgatitjes s\u00eb kuadrove. Specialist\u00ebt q\u00eb plot\u00ebsojn\u00eb k\u00ebto k\u00ebrkesa aktualisht jan\u00eb t\u00eb pak\u00ebt n\u00eb tregun e pun\u00ebs dhe jan\u00eb t\u00eb \u00e7muar si ari.<\/p>\n<h2>N\u00eb lidhje me kompetencat<\/h2>\n<p>\nN\u00eb teori, t\u00eb gjitha detyrat e MLOps mund t\u00eb zgjidh\u00ebn me mjete klasike DevOps dhe pa iu drejtuar zgjerimit t\u00eb specializuar t\u00eb modelit t\u00eb rolit. Si\u00e7 e p\u00ebrmend\u00ebm m\u00eb par\u00eb, data scientist duhet t\u00eb jet\u00eb jo vet\u00ebm matematicien dhe specialist n\u00eb analiz\u00ebn e t\u00eb dh\u00ebnave, por edhe guru i t\u00eb gjith\u00eb pipeline-it - ai \u00ebsht\u00eb p\u00ebrgjegj\u00ebs p\u00ebr zhvillimin e arkitektur\u00ebs, programimin e modeleve n\u00eb disa gjuh\u00eb var\u00ebsisht nga arkitektura, p\u00ebrgatitjen e vitrin\u00ebs s\u00eb t\u00eb dh\u00ebnave dhe deploymin e aplikacionit vet\u00eb. Megjithat\u00eb, krijimi i infrastruktur\u00ebs teknologjike, e cila realizohet n\u00eb procesin e plot\u00eb MLOps, z\u00eb deri n\u00eb 80% t\u00eb pun\u00ebs, q\u00eb do t\u00eb thot\u00eb se nj\u00eb matematicien i kualifikuar, i cili \u00ebsht\u00eb nj\u00eb Data Scientist i kualifikuar, do t'i kushtoj\u00eb vet\u00ebm 20% t\u00eb koh\u00ebs s\u00eb tij specialitetit t\u00eb tij. Prandaj, ndarja e rolit t\u00eb specialist\u00ebve, q\u00eb realizojn\u00eb procesin e integrimit t\u00eb modeleve t\u00eb m\u00ebsimit t\u00eb makin\u00ebs, b\u00ebhet jetike.\u00a0<\/p>\n<p>Sa sa e detajuar roli, varet nga shkalla e nd\u00ebrmarrjes. Nj\u00eb gj\u00eb \u00ebsht\u00eb kur n\u00eb nj\u00eb startup ka nj\u00eb specialist, pun\u00ebtor i gatsh\u00ebm p\u00ebr t\u00eb gjitha, vet\u00eb inxhinier, arkitekt dhe DevOps. Krejt ndryshe \u00ebsht\u00eb kur n\u00eb nj\u00eb nd\u00ebrmarrje t\u00eb madhe, t\u00eb gjitha proceset e zhvillimit t\u00eb modeleve p\u00ebrqendrohen tek disa specialist\u00eb t\u00eb nivelit t\u00eb lart\u00eb n\u00eb Data Science, nd\u00ebrsa programuesi apo specialisti i menaxhimit t\u00eb bazave t\u00eb dh\u00ebnave \u2014 \u00ebsht\u00eb nj\u00eb kompetenc\u00eb m\u00eb e p\u00ebrdorur dhe m\u00eb e lir\u00eb n\u00eb tregun e pun\u00ebs \u2014 q\u00eb mund t\u00eb merr pjes\u00ebn m\u00eb t\u00eb madhe t\u00eb detyrave rutin\u00eb.<\/p>\n<p>K\u00ebshtu, ku kalon kufiri n\u00eb zgjedhjen e specialist\u00ebve p\u00ebr t\u00eb siguruar procesin e MLOps dhe si \u00ebsht\u00eb organizuar procesi i operacionalizimit t\u00eb modeleve t\u00eb zhvilluara, ndikon drejtp\u00ebrdrejt n\u00eb shpejt\u00ebsin\u00eb dhe cil\u00ebsin\u00eb e modeleve t\u00eb zhvilluara, performanc\u00ebn e ekipit dhe mikroklim\u00ebn aty.<\/p>\n<h2>\u00c7far\u00eb \u00ebsht\u00eb b\u00ebr\u00eb tashm\u00eb nga ekipi yn\u00eb<\/h2>\n<p>\nPak koh\u00eb m\u00eb par\u00eb, filluam t\u00eb nd\u00ebrtojm\u00eb struktur\u00ebn e kompetencave dhe proceset e MLOps. Por tashm\u00eb, n\u00eb faz\u00ebn e testimit t\u00eb MVP-s\u00eb jan\u00eb projektet tona p\u00ebr menaxhimin e ciklit t\u00eb jet\u00ebs s\u00eb modeleve dhe p\u00ebrdorimin e modeleve si sh\u00ebrbim.<\/p>\n<p>Gjithashtu, ne identifikuam struktur\u00ebn optimale t\u00eb kompetencave p\u00ebr nj\u00eb nd\u00ebrmarrje t\u00eb madhe dhe struktur\u00ebn organizative t\u00eb bashk\u00ebpunimit mes t\u00eb gjith\u00eb pjes\u00ebmarr\u00ebsve t\u00eb procesit. U organizuan ekipe Agile q\u00eb zgjidhin detyra p\u00ebr t\u00eb gjith\u00eb spektrin e k\u00ebrkesave t\u00eb biznesit, si dhe u vendos nj\u00eb proces i bashk\u00ebpunimit me ekipet e projektit p\u00ebr nd\u00ebrtimin e platformave dhe infrastruktur\u00ebs, q\u00eb \u00ebsht\u00eb baza e nd\u00ebrtes\u00ebs s\u00eb MLOps.<\/p>\n<h2>Pyetje p\u00ebr t\u00eb ardhmen<\/h2>\n<p>\nMLOps \u00ebsht\u00eb nj\u00eb fush\u00eb n\u00eb zhvillim, e cila p\u00ebrjeton munges\u00eb kompetencash dhe n\u00eb t\u00eb ardhmen do t\u00eb merr drejtim. Deri tani, \u00ebsht\u00eb m\u00eb mir\u00eb t\u00eb fillojm\u00eb nga praktikat dhe p\u00ebrvojat e DevOps. Q\u00ebllimi kryesor i MLOps \u00ebsht\u00eb p\u00ebrdorimi m\u00eb efektiv i modeleve ML p\u00ebr zgjidhjen e detyrave t\u00eb biznesit. Por, k\u00ebt\u00eb situat\u00eb shoq\u00ebrojn\u00eb shum\u00eb pyetje:<\/p>\n<ul>\n<li>Si t\u00eb shkurtosh koh\u00ebn p\u00ebr t\u00eb nxjerr\u00eb modelet n\u00eb prodhim?\n<\/li>\n<li>Si t\u00eb zvog\u00ebloni f\u00ebrkimet burokratike mes ekipeve t\u00eb ndryshme dhe t\u00eb rritni fokusimin n\u00eb bashk\u00ebpunim?\n<\/li>\n<li>Si t\u00eb monitoroni modelet, menaxhoni versionet dhe organizoni nj\u00eb monitorim efektiv?\n<\/li>\n<li>Si t\u00eb krijoni nj\u00eb cik\u00ebl t\u00eb v\u00ebrtet\u00eb jet\u00ebsor p\u00ebr nj\u00eb model ML bashk\u00ebkohor?\n<\/li>\n<li>Si t\u00eb standardizoni procesin e m\u00ebsimit t\u00eb makinave?\n<\/li>\n<\/ul>\n<p>\nP\u00ebrgjigjet e k\u00ebtyre pyetjeve do t\u00eb ndikojn\u00eb shum\u00eb n\u00eb shkall\u00ebn n\u00eb t\u00eb cil\u00ebn MLOps do t\u00eb zbuloj\u00eb plot\u00ebsisht potencialin e tij.<br \/>\n<br \/>Burimi: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/vtb\/blog\/508012\/\">habr.com<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0412 2018 \u0433\u043e\u0434\u0443 \u0432 \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0445 \u043a\u0440\u0443\u0433\u0430\u0445 \u0438 \u043d\u0430 \u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u044f\u0445, \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u043d\u044b\u0445 AI, \u043f\u043e\u044f\u0432\u0438\u043b\u043e\u0441\u044c \u043f\u043e\u043d\u044f\u0442\u0438\u0435 MLOps, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0431\u044b\u0441\u0442\u0440\u043e \u0437\u0430\u043a\u0440\u0435\u043f\u0438\u043b\u043e\u0441\u044c \u0432 \u043e\u0442\u0440\u0430\u0441\u043b\u0438 \u0438 \u0441\u0435\u0439\u0447\u0430\u0441 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0441\u0430\u043c\u043e\u0441\u0442\u043e\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435. \u0412 \u043f\u0435\u0440\u0441\u043f\u0435\u043a\u0442\u0438\u0432\u0435 MLOps \u043c\u043e\u0436\u0435\u0442 \u0441\u0442\u0430\u0442\u044c \u043e\u0434\u043d\u043e\u0439 \u0438\u0437 \u043d\u0430\u0438\u0431\u043e\u043b\u0435\u0435 \u0432\u043e\u0441\u0442\u0440\u0435\u0431\u043e\u0432\u0430\u043d\u043d\u044b\u0445 \u0441\u0444\u0435\u0440 \u0432 IT. \u0427\u0442\u043e \u0436\u0435 \u044d\u0442\u043e \u0442\u0430\u043a\u043e\u0435 \u0438 \u0441 \u0447\u0435\u043c \u0435\u0433\u043e \u0435\u0434\u044f\u0442, \u0440\u0430\u0437\u0431\u0438\u0440\u0430\u0435\u043c\u0441\u044f \u043f\u043e\u0434 \u043a\u0430\u0442\u043e\u043c. \u0427\u0442\u043e \u0442\u0430\u043a\u043e\u0435 MLOps MLOps (\u043e\u0431\u044a\u0435\u0434\u0438\u043d\u0435\u043d\u0438\u0435 [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":86524,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-86523","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.0.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0412 2018 \u0433\u043e\u0434\u0443 \u0432 \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0445 \u043a\u0440\u0443\u0433\u0430\u0445 \u0438 \u043d\u0430 \u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u044f\u0445, \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u043d\u044b\u0445 AI, \u043f\u043e\u044f\u0432\u0438\u043b\u043e\u0441\u044c \u043f\u043e\u043d\u044f\u0442\u0438\u0435 MLOps, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0431\u044b\u0441\u0442\u0440\u043e \u0437\u0430\u043a\u0440\u0435\u043f\u0438\u043b\u043e\u0441\u044c \u0432 \u043e\u0442\u0440\u0430\u0441\u043b\u0438 \u0438 \u0441\u0435\u0439\u0447\u0430\u0441 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0441\u0430\u043c\u043e\u0441\u0442\u043e\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435.\" \/>\n\t<meta name=\"robots\" content=\"max-image-preview:large\" \/>\n\t<meta name=\"author\" content=\"Yuri Gagarin\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/prohoster.info\/sq\/blog\/administrirovanie\/mlops-devops-v-mire-machine-learning\" \/>\n\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.0.1\" \/>\n\t\t<meta property=\"og:locale\" content=\"sq_AL\" \/>\n\t\t<meta property=\"og:site_name\" content=\"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b\" \/>\n\t\t<meta property=\"og:type\" content=\"article\" \/>\n\t\t<meta property=\"og:title\" content=\"\ud83e\udd47MLOps: DevOps \u0432 \u043c\u0438\u0440\u0435 Machine Learning | ProHoster\" \/>\n\t\t<meta property=\"og:description\" content=\"\u0412 2018 \u0433\u043e\u0434\u0443 \u0432 \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0445 \u043a\u0440\u0443\u0433\u0430\u0445 \u0438 \u043d\u0430 \u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u044f\u0445, \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u043d\u044b\u0445 AI, \u043f\u043e\u044f\u0432\u0438\u043b\u043e\u0441\u044c \u043f\u043e\u043d\u044f\u0442\u0438\u0435 MLOps, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0431\u044b\u0441\u0442\u0440\u043e \u0437\u0430\u043a\u0440\u0435\u043f\u0438\u043b\u043e\u0441\u044c \u0432 \u043e\u0442\u0440\u0430\u0441\u043b\u0438 \u0438 \u0441\u0435\u0439\u0447\u0430\u0441 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0441\u0430\u043c\u043e\u0441\u0442\u043e\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435.\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/prohoster.info\/sq\/blog\/administrirovanie\/mlops-devops-v-mire-machine-learning\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg\" \/>\n\t\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t\t<meta property=\"og:image:height\" content=\"350\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2020-06-26T05:42:14+00:00\" \/>\n\t\t<meta property=\"article:modified_time\" content=\"2020-06-26T05:42:14+00:00\" \/>\n\t\t<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<meta property=\"article:author\" content=\"https:\/\/www.facebook.com\/prohoster\" \/>\n\t\t<!-- All in One SEO -->\n\n","aioseo_head_json":{"title":"\ud83e\udd47MLOps: DevOps n\u00eb bot\u00ebn e Machine Learning | ProHoster","description":"N\u00eb vitin 2018, n\u00eb rrethana profesionale dhe n\u00eb konferenca t\u00eb tematik\u00ebs lidhur me AI, u shfaq koncepti MLOps, i cili u konsolidua shpejt n\u00eb industrin\u00eb dhe tani po zhvillohet si nj\u00eb drejtim i pavarur.","canonical_url":"https:\/\/prohoster.info\/sq\/blog\/administrirovanie\/mlops-devops-v-mire-machine-learning","robots":"max-image-preview:large","keywords":"","webmasterTools":{"miscellaneous":""},"schema":null,"og:locale":"sq_AL","og:site_name":"ProHoster | \u041a\u0443\u043f\u0438\u0442\u044c \u043d\u0430\u0434\u0435\u0436\u043d\u044b\u0439 \u0445\u043e\u0441\u0442\u0438\u043d\u0433 \u0434\u043b\u044f \u0441\u0430\u0439\u0442\u043e\u0432 \u0441 \u0437\u0430\u0449\u0438\u0442\u043e\u0439 \u043e\u0442 DDoS, VPS VDS \u0441\u0435\u0440\u0432\u0435\u0440\u044b","og:type":"article","og:title":"\ud83e\udd47MLOps: DevOps \u0432 \u043c\u0438\u0440\u0435 Machine Learning | ProHoster","og:description":"\u0412 2018 \u0433\u043e\u0434\u0443 \u0432 \u043f\u0440\u043e\u0444\u0435\u0441\u0441\u0438\u043e\u043d\u0430\u043b\u044c\u043d\u044b\u0445 \u043a\u0440\u0443\u0433\u0430\u0445 \u0438 \u043d\u0430 \u0442\u0435\u043c\u0430\u0442\u0438\u0447\u0435\u0441\u043a\u0438\u0445 \u043a\u043e\u043d\u0444\u0435\u0440\u0435\u043d\u0446\u0438\u044f\u0445, \u043f\u043e\u0441\u0432\u044f\u0449\u0435\u043d\u043d\u044b\u0445 AI, \u043f\u043e\u044f\u0432\u0438\u043b\u043e\u0441\u044c \u043f\u043e\u043d\u044f\u0442\u0438\u0435 MLOps, \u043a\u043e\u0442\u043e\u0440\u043e\u0435 \u0431\u044b\u0441\u0442\u0440\u043e \u0437\u0430\u043a\u0440\u0435\u043f\u0438\u043b\u043e\u0441\u044c \u0432 \u043e\u0442\u0440\u0430\u0441\u043b\u0438 \u0438 \u0441\u0435\u0439\u0447\u0430\u0441 \u0440\u0430\u0437\u0432\u0438\u0432\u0430\u0435\u0442\u0441\u044f \u043a\u0430\u043a \u0441\u0430\u043c\u043e\u0441\u0442\u043e\u044f\u0442\u0435\u043b\u044c\u043d\u043e\u0435 \u043d\u0430\u043f\u0440\u0430\u0432\u043b\u0435\u043d\u0438\u0435.","og:url":"https:\/\/prohoster.info\/sq\/blog\/administrirovanie\/mlops-devops-v-mire-machine-learning","og:image":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:secure_url":"https:\/\/prohoster.info\/wp-content\/uploads\/2021\/11\/logo-350.jpg","og:image:width":350,"og:image:height":350,"article:published_time":"2020-06-26T05:42:14+00:00","article:modified_time":"2020-06-26T05:42:14+00:00","article:publisher":"https:\/\/www.facebook.com\/prohoster","article:author":"https:\/\/www.facebook.com\/prohoster"},"aioseo_meta_data":{"post_id":"86523","title":null,"description":null,"keywords":null,"keyphrases":null,"primary_term":null,"canonical_url":null,"og_title":null,"og_description":null,"og_object_type":"default","og_image_type":"default","og_image_url":null,"og_image_width":null,"og_image_height":null,"og_image_custom_url":null,"og_image_custom_fields":null,"og_video":null,"og_custom_url":null,"og_article_section":null,"og_article_tags":null,"twitter_use_og":false,"twitter_card":"default","twitter_image_type":"default","twitter_image_url":null,"twitter_image_custom_url":null,"twitter_image_custom_fields":null,"twitter_title":null,"twitter_description":null,"schema":{"blockGraphs":[],"customGraphs":[],"default":{"data":{"Article":[],"Course":[],"Dataset":[],"FAQPage":[],"Movie":[],"Person":[],"Product":[],"ProductReview":[],"Car":[],"Recipe":[],"Service":[],"SoftwareApplication":[],"WebPage":[]},"graphName":"","isEnabled":true},"graphs":[]},"schema_type":null,"schema_type_options":null,"pillar_content":false,"robots_default":true,"robots_noindex":false,"robots_noarchive":false,"robots_nosnippet":false,"robots_nofollow":false,"robots_noimageindex":false,"robots_noodp":false,"robots_notranslate":false,"robots_max_snippet":null,"robots_max_videopreview":null,"robots_max_imagepreview":"large","priority":null,"frequency":null,"local_seo":null,"seo_analyzer_scan_date":null,"breadcrumb_settings":null,"limit_modified_date":false,"reviewed_by":null,"ai":null,"created":"2021-02-28 14:13:23","updated":"2022-09-28 09:22:54","focus_keyword":null,"additional_keywords":null,"truseo_locale":null},"gt_translate_keys":[{"key":"link","format":"url"}],"_links":{"self":[{"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/posts\/86523","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/comments?post=86523"}],"version-history":[{"count":0,"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/posts\/86523\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/media\/86524"}],"wp:attachment":[{"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/media?parent=86523"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/categories?post=86523"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/prohoster.info\/sq\/wp-json\/wp\/v2\/tags?post=86523"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}