{"id":53143,"date":"2019-11-24T00:00:00","date_gmt":"2019-11-23T21:00:00","guid":{"rendered":"https:\/\/prohoster.info\/blog\/blog_prohoster\/ekspluatatsiya-mashinnogo-obucheniya-v-pochte-mail-ru"},"modified":"2020-02-18T14:01:00","modified_gmt":"2020-02-18T11:01:00","slug":"ekspluatatsiya-mashinnogo-obucheniya-v-pochte-mail-ru","status":"publish","type":"post","link":"https:\/\/prohoster.info\/sq\/blog\/administrirovanie\/ekspluatatsiya-mashinnogo-obucheniya-v-pochte-mail-ru","title":{"rendered":"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/ee5728dccb94b849ad3bfd9cf84ad74d.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\n<i>Nga motivet e paraqitjeve t\u00eb mia n\u00eb Highload++ dhe DataFest Minsk 2019.<\/i><\/p>\n<p>P\u00ebr shum\u00eb njer\u00ebz sot, posta \u00ebsht\u00eb nj\u00eb pjes\u00eb e pandashme e jet\u00ebs n\u00eb internet. Me ndihm\u00ebn e saj, ne zhvillojm\u00eb korrespondenc\u00eb biznesi, ruajm\u00eb t\u00eb gjitha llojet e informacionit t\u00eb r\u00ebnd\u00ebsish\u00ebm q\u00eb lidhet me financat, rezervimin e hoteleve, porosin\u00eb e produkteve dhe shum\u00eb t\u00eb tjera. N\u00eb mes t\u00eb vitit 2018, formulua strategjia e zhvillimit t\u00eb produktit t\u00eb post\u00ebs. Si duhet t\u00eb jet\u00eb posta moderne?<\/p>\n<p>Posta duhet t\u00eb jet\u00eb <b>inteligjente<\/b>, dometh\u00ebn\u00eb t\u00eb ndihmoj\u00eb p\u00ebrdoruesit t\u00eb orientohen n\u00eb volumin n\u00eb rritje t\u00eb informacionit: t\u00eb filtroj\u00eb, t\u00eb strukturoj\u00eb dhe t\u00eb siguroj\u00eb at\u00eb n\u00eb m\u00ebnyr\u00ebn m\u00eb t\u00eb p\u00ebrshtatshme. Ajo duhet t\u00eb jet\u00eb <b>e dobishme<\/b>, duke lejuar q\u00eb n\u00eb kutin\u00eb postare t\u00eb zgjidhen detyra t\u00eb ndryshme, p\u00ebr shembull, t\u00eb paguhen gjobat (funksion, t\u00eb cilin, p\u00ebr fat t\u00eb keq, e p\u00ebrdor). Dhe, natyrisht, posta duhet t\u00eb siguroj\u00eb mbrojtje informacioni, duke filtruar spamin dhe duke mbrojtur nga thyerjet, dmth t\u00eb jet\u00eb <b>e sigurt<\/b>.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\nK\u00ebto drejtime p\u00ebrcaktojn\u00eb nj\u00eb s\u00ebr\u00eb detyrash ky\u00e7e, shum\u00eb prej t\u00eb cilave mund t\u00eb zgjidhen n\u00eb m\u00ebnyr\u00eb efektive me ndihm\u00ebn e m\u00ebsimdh\u00ebnies t\u00eb automatik\u00ebs. Ja disa shembuj t\u00eb funksionaliteteve q\u00eb tashm\u00eb jan\u00eb n\u00eb pun\u00eb, t\u00eb zhvilluara si pjes\u00eb e strategjis\u00eb \u2014 nga nj\u00eb p\u00ebr \u00e7do drejtim.<\/p>\n<ul>\n<li><b>P\u00ebrgjigje e Men\u00e7ur<\/b>. N\u00eb post\u00eb ekziston funksioni i p\u00ebrgjigjes s\u00eb men\u00e7ur. Rrjeti nervor analizon tekstin e emailit, kupton kuptimin dhe q\u00ebllimin e tij, dhe si rezultat ofron tre opsione m\u00eb t\u00eb p\u00ebrshtatshme t\u00eb p\u00ebrgjigjes: pozitive, negative dhe neutrale. Kjo ndihmon q\u00eb t\u00eb kursethni ndjesh\u00ebm koh\u00eb gjat\u00eb p\u00ebrgjigjeve t\u00eb emaileve, si dhe shpesh ndihmon q\u00eb t\u00eb p\u00ebrgjigjeni n\u00eb m\u00ebnyr\u00eb kreative dhe arg\u00ebtuese p\u00ebr veten.\n<\/li>\n<li><b>Grupimi i emaileve<\/b>, q\u00eb lidhen me porosit\u00eb n\u00eb dyqanet online. Ne shpesh b\u00ebjm\u00eb blerje n\u00eb internet, dhe zakonisht, dyqanet mund t\u00eb d\u00ebrgojn\u00eb disa letra p\u00ebr \u00e7do porosi. P\u00ebr shembull, nga AliExpress, sh\u00ebrbimi m\u00eb i madh, vijn\u00eb shum\u00eb letra p\u00ebr nj\u00eb porosi t\u00eb vetme, dhe ne llogarit\u00ebm se n\u00eb rastin m\u00eb ekstrem, numri i tyre mund t\u00eb arrij\u00eb deri n\u00eb 29. Prandaj, me modele t\u00eb Njoftimit t\u00eb Entiteteve, ne nxjerrim numrin e porosis\u00eb dhe informacion t\u00eb tjera nga teksti dhe i grupojm\u00eb t\u00eb gjitha letrat n\u00eb nj\u00eb tem\u00eb. Ne gjithashtu tregojm\u00eb informacionin kryesor mbi porosin\u00eb n\u00eb nj\u00eb panel t\u00eb ve\u00e7ant\u00eb, q\u00eb leht\u00ebson pun\u00ebn me k\u00ebt\u00eb lloj letre.\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/8b37b0bf0c0d152498027bc25d476128.jpg\" style=\"display:block;margin: 0 auto;\" \/>\n<\/li>\n<li><b>Antifishing<\/b>. Phishing-u \u00ebsht\u00eb nj\u00eb lloj mashtrimi ve\u00e7an\u00ebrisht i rreziksh\u00ebm, me t\u00eb cilin keqb\u00ebr\u00ebsit p\u00ebrpiqen t\u00eb marrin informacionin financiar (p\u00ebrfshir\u00eb kartat e bank\u00ebs s\u00eb p\u00ebrdoruesit) dhe emrat e p\u00ebrdoruesve. K\u00ebto letra imitojn\u00eb letra t\u00eb v\u00ebrteta, t\u00eb d\u00ebrguara nga sh\u00ebrbimi, duke p\u00ebrfshir\u00eb vizualisht. Prandaj, me ndihm\u00ebn e Vizionit t\u00eb Kompjuterit, ne njohim logot dhe stilin e dizajnit t\u00eb letrave nga kompani t\u00eb m\u00ebdha (p\u00ebr shembull, Mail.ru, Sber, Alfa) dhe e marrim k\u00ebt\u00eb parasysh p\u00ebrve\u00e7 tekstit dhe karakteristikave t\u00eb tjera n\u00eb klasifikator\u00ebt tan\u00eb t\u00eb spam-it dhe phishing-ut.\n<\/li>\n<\/ul>\n<p><\/p>\n<h2>M\u00ebsimi i makineris\u00eb<\/h2>\n<p>\nPak m\u00eb shum\u00eb rreth m\u00ebsimit t\u00eb makinave n\u00eb post\u00eb n\u00eb p\u00ebrgjith\u00ebsi. Posta \u00ebsht\u00eb nj\u00eb sistem me ngarkes\u00eb t\u00eb lart\u00eb: p\u00ebrmes server\u00ebve tan\u00eb kalojn\u00eb, n\u00eb mesatarisht, 1.5 miliard mesazhesh n\u00eb dit\u00eb p\u00ebr 30 milion p\u00ebrdorues DAU. Ofrojn\u00eb t\u00eb gjith\u00eb funksionet dhe karakteristikat e nevojshme rreth 30 sistemeve t\u00eb m\u00ebsimit t\u00eb makinave. <\/p>\n<p>\u00c7do mesazh kalon p\u00ebrmes nj\u00eb linje t\u00eb plot\u00eb klasifikimi. S\u00eb pari, ne blokojm\u00eb spam-in dhe l\u00ebm\u00eb mesazhet e mira. P\u00ebrdoruesit shpesh nuk e v\u00ebrejn\u00eb pun\u00ebn e anti-spam-it, sepse 95-99% e spam-it nuk arrin as n\u00eb dosjen p\u00ebrkat\u00ebse. Aft\u00ebsia p\u00ebr t\u00eb identifikuar spam-in \u00ebsht\u00eb nj\u00eb pjes\u00eb shum\u00eb e r\u00ebnd\u00ebsishme e sistemit ton\u00eb dhe m\u00eb e komplikuara, pasi n\u00eb fush\u00ebn e anti-spam-it ka nj\u00eb adaptim t\u00eb vazhduesh\u00ebm midis sistemeve mbrojt\u00ebse dhe sulmuese, \u00e7ka paraqet nj\u00eb sfid\u00eb inxhinierike t\u00eb vazhdueshme p\u00ebr ekipin ton\u00eb.<\/p>\n<p>M\u00eb pas, ne ndarim mesazhet nga njer\u00ebzit dhe robot\u00ebt. Mesazhet nga njer\u00ebzit jan\u00eb m\u00eb t\u00eb r\u00ebnd\u00ebsishmet, ndaj p\u00ebr to ofrojm\u00eb funksione si Smart Reply. Mesazhet nga robot\u00ebt ndahen n\u00eb dy pjes\u00eb: ato transaksionale \u2014 q\u00eb jan\u00eb mesazhe t\u00eb r\u00ebnd\u00ebsishme nga sh\u00ebrbimet, si p\u00ebr shembull, konfirmimet e blerjeve ose rezervimeve n\u00eb hotel, financat, dhe ato informuese \u2014 q\u00eb jan\u00eb reklama biznesi, zbritje. <\/p>\n<p>Ne mendojm\u00eb se letrat tregtare jan\u00eb Po aq t\u00eb r\u00ebnd\u00ebsishme sa korrespondenca personale. Ato duhet t\u00eb jen\u00eb n\u00eb dispozicion, pasi shpesh \u00ebsht\u00eb e nevojshme t\u00eb gjejm\u00eb shpejt informacion rreth porosis\u00eb ose rezervimeve t\u00eb fluturimeve, nd\u00ebrsa ne humbasim koh\u00eb duke k\u00ebrkuar k\u00ebto letra. Prandaj, p\u00ebr komfortin ton\u00eb, ne i ndajm\u00eb ato automatikisht n\u00eb gjasht\u00eb kategori kryesore: udh\u00ebtimet, porosit\u00eb, financat, biletat, regjistrimet dhe, p\u00ebrfundimisht, d\u00ebnimet.<\/p>\n<p>Letrat informacionale jan\u00eb grupi m\u00eb i madh dhe ndoshta m\u00eb pak i r\u00ebnd\u00ebsish\u00ebm, q\u00eb nuk k\u00ebrkon nj\u00eb reagim t\u00eb menj\u00ebhersh\u00ebm, pasi asgj\u00eb thelb\u00ebsore nuk do t\u00eb ndryshoj\u00eb n\u00eb jet\u00ebn e p\u00ebrdoruesit n\u00ebse ai nuk e lexon nj\u00eb let\u00ebr t\u00eb till\u00eb. N\u00eb nd\u00ebrfaqen ton\u00eb t\u00eb re, ne i grumbullojm\u00eb ato n\u00eb dy thread-e: rrjetet sociale dhe d\u00ebrgesat, duke e pastruar vizualisht kutin\u00eb dhe l\u00ebn\u00eb n\u00eb dukje vet\u00ebm letrat e r\u00ebnd\u00ebsishme.<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/175773879daaa29d1542a98070cb7972.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>Ekspluatimi<\/h3>\n<p>\nNj\u00eb num\u00ebr i madh sistemesh sjell shum\u00eb v\u00ebshtir\u00ebsi n\u00eb funksionim. Modelet degradohen me kalimin e koh\u00ebs, si \u00e7do software tjet\u00ebr: shfaqen defekte, makinat refuzojn\u00eb t\u00eb funksionojn\u00eb, shkruhet kod i keq. Gjithashtu, t\u00eb dh\u00ebnat ndryshojn\u00eb vazhdimisht: shtohen t\u00eb reja, transformohet modeli i sjelljes s\u00eb p\u00ebrdoruesve etj., prandaj modelin pa mb\u00ebshtetje t\u00eb duhur do t\u00eb filloj\u00eb t\u00eb funksionoj\u00eb gjithnj\u00eb e m\u00eb keq me kalimin e koh\u00ebs. <\/p>\n<p>Nuk duhet harruar se sa m\u00eb shum\u00eb t\u00eb thellohet m\u00ebsimi i makinerive n\u00eb jet\u00ebn e p\u00ebrdoruesve, aq m\u00eb shum\u00eb ndikim kan\u00eb ata n\u00eb ekosistem, dhe, si pasoj\u00eb, aq m\u00eb shum\u00eb humbje financiare ose fitime mund t\u00eb ken\u00eb lojtar\u00ebt e tregut. Prandaj, n\u00eb nj\u00eb num\u00ebr gjithnj\u00eb e n\u00eb rritje fushash, lojtar\u00ebt adaptohen p\u00ebr t\u00eb punuar me algoritmet e ML (shembuj klasik\u00eb \u2014 reklama, k\u00ebrkimi dhe tashm\u00eb e p\u00ebrmendur anti-spam).<\/p>\n<p>Gjithashtu, detyrat e m\u00ebsimit t\u00eb makinerive kan\u00eb nj\u00eb ve\u00e7ori: \u00e7do ndryshim, madje edhe m\u00eb i vogli, n\u00eb sistem mund t\u00eb sjell\u00eb shum\u00eb pun\u00eb me modelin: pun\u00eb me t\u00eb dh\u00ebnat, ri-trajnimi, vendosja, e cila mund t\u00eb zgjas\u00eb p\u00ebr jav\u00eb ose muaj. Prandaj, sa m\u00eb shpejt t\u00eb ndryshoj\u00eb mjedisi n\u00eb t\u00eb cilin operojn\u00eb modelet tuaja, aq m\u00eb shum\u00eb p\u00ebrpjekje k\u00ebrkon mb\u00ebshtetje e tyre. Ekipa mund t\u00eb krijoj\u00eb shum\u00eb sisteme dhe t\u00eb g\u00ebzohet p\u00ebr k\u00ebt\u00eb, pastaj t\u00eb shpenzoj\u00eb pothuajse t\u00eb gjitha burimet e saj p\u00ebr mb\u00ebshtetje, pa mund\u00ebsi p\u00ebr t\u00eb b\u00ebr\u00eb di\u00e7ka t\u00eb re. Nj\u00eb situat\u00eb t\u00eb till\u00eb ne u p\u00ebrball\u00ebm nj\u00eb her\u00eb n\u00eb ekipin ton\u00eb t\u00eb anti-spamit. Dhe arrit\u00ebm n\u00eb p\u00ebrfundimin e qart\u00eb se mb\u00ebshtetje duhet t\u00eb automatizohet.<\/p>\n<h3>Automatizimi<\/h3>\n<p>\n\u00c7far\u00eb mund t\u00eb automatizohet? N\u00eb t\u00eb v\u00ebrtet\u00eb, pothuajse gjith\u00e7ka. Kam p\u00ebrzgjidhur kat\u00ebr drejtime q\u00eb p\u00ebrkufizojn\u00eb infrastruktur\u00ebn e m\u00ebsimit t\u00eb makinerive:<\/p>\n<ul>\n<li>mbledhja e t\u00eb dh\u00ebnave;\n<\/li>\n<li>ri-trajnimi;\n<\/li>\n<li>vendosja;\n<\/li>\n<li>testimi &amp; monitorimi.\n<\/li>\n<\/ul>\n<p>\nN\u00ebse ambienti \u00ebsht\u00eb i paq\u00ebndruesh\u00ebm dhe vazhdimisht ndryshon, at\u00ebher\u00eb t\u00ebr\u00eb infrastruktura rreth modelit b\u00ebhet shum\u00eb m\u00eb e r\u00ebnd\u00ebsishme se vet\u00eb modeli. Mund t\u00eb jet\u00eb nj\u00eb klasifikues i thjesht\u00eb linear, por n\u00ebse e ushqeni si duhet me karakteristika dhe krijoni nj\u00eb feedback t\u00eb mir\u00eb nga p\u00ebrdoruesit, at\u00ebher\u00eb ai do t\u00eb funksionoj\u00eb shum\u00eb m\u00eb mir\u00eb se modelet m\u00eb t\u00eb avancuara me t\u00eb gjitha pompimet.<\/p>\n<h4>Cikli i feedback-ut<\/h4>\n<p>\nKy cik\u00ebl p\u00ebrfshin mbledhjen e t\u00eb dh\u00ebnave, ri-trajnimin dhe p\u00ebrdorimin \u2014 n\u00eb thelb, gjith\u00eb ciklin e p\u00ebrdit\u00ebsimit t\u00eb modelit. Pse \u00ebsht\u00eb e r\u00ebnd\u00ebsishme? Shikoni grafikun e regjistrimeve n\u00eb post\u00eb:<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/f3c98bd49a101754cf299821a40e2b8e.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nProgramuesi i m\u00ebsimit t\u00eb makinave ka implementuar nj\u00eb model antibot, i cili nuk lejon q\u00eb bot\u00ebt t\u00eb regjistrohen n\u00eb post\u00eb. Grafiku bie deri n\u00eb nj\u00eb vler\u00eb ku mbeten vet\u00ebm p\u00ebrdoruesit e v\u00ebrtet\u00eb. E gjith\u00eb kjo \u00ebsht\u00eb shk\u00eblqyer! Por kalojn\u00eb kat\u00ebr or\u00eb, bot-kreator\u00ebt rregullojn\u00eb skenar\u00ebt e tyre, dhe gjith\u00e7ka kthehet n\u00eb pik\u00ebn e fillimit. N\u00eb k\u00ebt\u00eb implementim, programuesi kaloi nj\u00eb muaj, duke shtuar karakteristika dhe ri-trajnuar modelin, por spameri arriti t\u00eb adaptohej brenda kat\u00ebr or\u00ebve.<\/p>\n<p>P\u00ebr t\u00eb mos qen\u00eb kaq painfully e v\u00ebshtir\u00eb dhe p\u00ebr t\u00eb mos pasur nevoj\u00eb t\u00eb rregullojm\u00eb gjith\u00e7ka m\u00eb von\u00eb, duhet t\u00eb mendojm\u00eb fillimisht se si do t\u00eb duket cikli i reagimit dhe \u00e7far\u00eb do t\u00eb b\u00ebjm\u00eb n\u00ebse ambienti ndryshon. Le t\u00eb fillojm\u00eb me mbledhjen e t\u00eb dh\u00ebnave \u2014 ky \u00ebsht\u00eb karburanti p\u00ebr algoritmet tona.<\/p>\n<h2>Mbledhja e t\u00eb dh\u00ebnave<\/h2>\n<p>\nE qart\u00eb se p\u00ebr rrjetet neurale moderne, sa m\u00eb shum\u00eb t\u00eb dh\u00ebna, aq m\u00eb mir\u00eb, dhe k\u00ebto jan\u00eb, n\u00eb thelb, t\u00eb gjeneruara nga p\u00ebrdoruesit e produktit. P\u00ebrdoruesit mund t\u00eb na ndihmojn\u00eb duke etiketuar t\u00eb dh\u00ebnat, por nuk duhet keqp\u00ebrdorur k\u00ebt\u00eb, sepse p\u00ebrdoruesit n\u00eb nj\u00eb moment do t'u m\u00ebrzitet t\u00eb m\u00ebsojn\u00eb modelet tuaja dhe do t\u00eb kalojn\u00eb n\u00eb nj\u00eb produkt tjet\u00ebr. <\/p>\n<p>Nj\u00eb nga gabimet m\u00eb t\u00eb shpeshta (k\u00ebtu po referohem tek Andrew Ng) \u2014 \u00ebsht\u00eb nj\u00eb orientim tep\u00ebr i fort\u00eb ndaj metrikave n\u00eb datasetin e testimit, n\u00eb vend t\u00eb reagimeve nga p\u00ebrdoruesi, i cili n\u00eb t\u00eb v\u00ebrtet\u00eb \u00ebsht\u00eb masa kryesore e cil\u00ebsis\u00eb, sepse ne krijojm\u00eb produktin p\u00ebr p\u00ebrdoruesin. N\u00ebse p\u00ebrdoruesit nuk e kuptojn\u00eb ose nuk e p\u00eblqejn\u00eb funksionimin e modelit, at\u00ebher\u00eb gjith\u00e7ka \u00ebsht\u00eb e kot\u00eb. <\/p>\n<p>Prandaj, p\u00ebrdoruesi gjithmon\u00eb duhet t\u00eb ket\u00eb mund\u00ebsin\u00eb t\u00eb votoj\u00eb, prandaj duhet t\u2019i ofrohet nj\u00eb mjet p\u00ebr feedback. N\u00ebse ne mendojm\u00eb se n\u00eb kuti ka ardhur nj\u00eb let\u00ebr q\u00eb i p\u00ebrket financave, duhet ta sh\u00ebnojm\u00eb at\u00eb si \u00abfinanc\u00eb\u00bb dhe t\u00eb pozicionojm\u00eb nj\u00eb buton q\u00eb p\u00ebrdoruesi mund t\u00eb shtyp\u00eb dhe t\u00eb thot\u00eb se kjo nuk \u00ebsht\u00eb financ\u00eb.<\/p>\n<h3>Cil\u00ebsia e feedback-ut<\/h3>\n<p>\nLe t\u00eb flasim p\u00ebr cil\u00ebsin\u00eb e feedback-ut t\u00eb p\u00ebrdoruesve. S\u00eb pari, ju dhe p\u00ebrdoruesi mund t\u00eb keni kuptime t\u00eb ndryshme p\u00ebr nj\u00eb koncept t\u00eb vet\u00ebm. P\u00ebr shembull, ju dhe menaxher\u00ebt e produktit mendoni se \u00abfinancat\u00bb jan\u00eb letra nga banka, nd\u00ebrsa p\u00ebrdoruesi mendon se letra nga gjyshja p\u00ebr pensionin p\u00ebrb\u00ebn gjithashtu financa. S\u00eb dyti, ka p\u00ebrdorues q\u00eb pa menduar e duan t\u00eb shtypin butona pa ndonj\u00eb logjik\u00eb. S\u00eb treti, p\u00ebrdoruesi mund t\u00eb ket\u00eb keqkuptime t\u00eb thella n\u00eb p\u00ebrfundimet e tij. Nj\u00eb shembull i qart\u00eb nga praktika jon\u00eb \u00ebsht\u00eb implementimi i klasifikuesit <noindex><a rel=\"nofollow\" href=\"https:\/\/ru.wikipedia.org\/wiki\/%D0%9D%D0%B8%D0%B3%D0%B5%D1%80%D0%B8%D0%B9%D1%81%D0%BA%D0%B8%D0%B5_%D0%BF%D0%B8%D1%81%D1%8C%D0%BC%D0%B0\">t\u00eb spamit nigerian<\/a><\/noindex>, nj\u00eb lloj shum\u00eb arg\u00ebtues spami, kur p\u00ebrdoruesit i ofrohet t\u00eb marr\u00eb disa miliona dollar\u00eb nga nj\u00eb kush\u00ebri i papritur q\u00eb \u00ebsht\u00eb gjetur n\u00eb Afrik\u00eb. Pas implementimit t\u00eb k\u00ebtij klasifikuesi, ne kontrolluam klikimet 'Jo spam' n\u00eb k\u00ebto mesazhe, dhe doli se 80% e tyre ishin spama t\u00eb shijshme nigeriane, q\u00eb tregon se p\u00ebrdoruesit mund t\u00eb jen\u00eb jasht\u00ebzakonisht besnik\u00eb.<\/p>\n<p>Dhe nuk duhet t\u00eb harrojm\u00eb se mbi butonat mund t\u00eb klikojn\u00eb jo vet\u00ebm njer\u00ebzit, por edhe disa bota q\u00eb b\u00ebjn\u00eb sikur jan\u00eb shfletues. K\u00ebshtu q\u00eb feedbacku i pap\u00ebrpunuar nuk \u00ebsht\u00eb i duhuri p\u00ebr trajnimin. \u00c7far\u00eb mund t\u00eb b\u00ebjm\u00eb me k\u00ebt\u00eb informacion?<\/p>\n<p>Ne zbatojm\u00eb dy qasje: <\/p>\n<ul>\n<li><b>Feedback nga ML i lidhur<\/b>. P\u00ebr shembull, ne kemi nj\u00eb sistem online anti-bot dhe si\u00e7 e p\u00ebrmenda m\u00eb par\u00eb, ai merr nj\u00eb vendim t\u00eb shpejt\u00eb n\u00eb baz\u00eb t\u00eb nj\u00eb numri t\u00eb kufizuar karakteristikash. Dhe ka nj\u00eb sistem t\u00eb dyt\u00eb, t\u00eb ngadalsh\u00ebm, q\u00eb punon post-faktum. Ai ka m\u00eb shum\u00eb t\u00eb dh\u00ebna p\u00ebr p\u00ebrdoruesin, p\u00ebr sjelljen e tij, etj. Si pasoj\u00eb, merren vendime m\u00eb t\u00eb balancuara, ndaj ai ka sakt\u00ebsi dhe plot\u00ebsi m\u00eb t\u00eb lart\u00eb. Diferenca n\u00eb funksionimin e k\u00ebtyre sistem\u00ebve mund t\u00eb redirected n\u00eb t\u00eb parin si t\u00eb dh\u00ebna p\u00ebr m\u00ebsim. N\u00eb k\u00ebt\u00eb m\u00ebnyr\u00eb, sistemi m\u00eb i thjesht\u00eb do t\u00eb p\u00ebrpiqet gjithmon\u00eb t\u00eb afrohet me performanc\u00ebn e sistemit m\u00eb kompleks.\n<\/li>\n<li><b>Klasifikimi i klikimeve<\/b>. Mund t\u00eb klasifikoni thjesht \u00e7do klikim t\u00eb p\u00ebrdoruesit, t\u00eb vler\u00ebsoni vlefshm\u00ebrin\u00eb e tij dhe mund\u00ebsin\u00eb e p\u00ebrdorimit. K\u00ebshtu veprojm\u00eb n\u00eb anti-spamin e post\u00ebs, duke p\u00ebrdorur karakteristikat e p\u00ebrdoruesit, historin\u00eb e tij, ve\u00e7orit\u00eb e d\u00ebrguesit, tekstin dhe rezultatet e klasifikuesve. N\u00eb fund marrim nj\u00eb sistem automatizues, q\u00eb vler\u00ebson feedback-un e p\u00ebrdoruesit. Dhe meqen\u00ebse ka nevoj\u00eb p\u00ebr t'u trajnuar shum\u00eb m\u00eb rrall\u00eb, puna e tij mund t\u00eb b\u00ebhet themelore p\u00ebr t\u00eb gjitha sistemet e tjera. Prioriteti kryesor n\u00eb k\u00ebt\u00eb model \u00ebsht\u00eb preciziteti, sepse trajnimi i modelit me t\u00eb dh\u00ebna t\u00eb pasakta sjell pasoja. \n<\/li>\n<\/ul>\n<p>\nNd\u00ebrsa ne pastrojm\u00eb t\u00eb dh\u00ebnat dhe m\u00ebsojm\u00eb m\u00eb tej sistemet tona ML, nuk duhet t\u00eb harrojm\u00eb p\u00ebr p\u00ebrdoruesit, sepse p\u00ebr ne mij\u00ebra, miliona gabime n\u00eb grafik jan\u00eb statistika, nd\u00ebrsa p\u00ebr p\u00ebrdoruesin \u00e7do problem \u00ebsht\u00eb nj\u00eb tragjedi. P\u00ebrve\u00e7 faktit q\u00eb p\u00ebrdoruesi duhet t\u00eb jetoj\u00eb me gabimin tuaj n\u00eb produkt, ai\/ajo pas dh\u00ebnies s\u00eb feedback-ut pret q\u00eb nj\u00eb situat\u00eb e till\u00eb t\u00eb mos p\u00ebrs\u00ebritet n\u00eb t\u00eb ardhmen. Prandaj, \u00ebsht\u00eb gjithmon\u00eb e r\u00ebnd\u00ebsishme t\u00eb ofroni p\u00ebrdoruesve jo vet\u00ebm mund\u00ebsin\u00eb p\u00ebr t\u00eb votuar, por edhe p\u00ebr t\u00eb korrigjuar sjelljen e sistemeve ML, duke krijuar, p\u00ebr shembull, heuristika personale p\u00ebr \u00e7do klikim feedback-u; n\u00eb rastin e post\u00ebs, kjo mund t\u00eb jet\u00eb mund\u00ebsia p\u00ebr t\u00eb filtruar letra t\u00eb tilla nga d\u00ebrguesi dhe titulli p\u00ebr k\u00ebt\u00eb p\u00ebrdorues.<\/p>\n<p>Po ashtu, \u00ebsht\u00eb e nevojshme q\u00eb n\u00eb baz\u00eb t\u00eb disa raporteve ose ankesave n\u00eb mb\u00ebshtetje t\u00eb krijohet model n\u00eb m\u00ebnyr\u00eb semi-automatikisht ose manualisht, k\u00ebshtu q\u00eb p\u00ebrdoruesit e tjer\u00eb mos t\u00eb vuajn\u00eb nga probleme t\u00eb ngjashme.<\/p>\n<h3>Heuristika p\u00ebr m\u00ebsim<\/h3>\n<p>\nMe t\u00eb dh\u00ebnat e k\u00ebsaj heuristike dhe shtr\u00ebngesave ka dy probleme. E para \u00ebsht\u00eb se numri gjithnj\u00eb n\u00eb rritje i shtr\u00ebngesave \u00ebsht\u00eb e v\u00ebshtir\u00eb p\u00ebr t'u mbajtur, p\u00ebr t\u00eb mos th\u00ebn\u00eb p\u00ebr cil\u00ebsin\u00eb dhe funksionimin e tyre n\u00eb distanc\u00eb t\u00eb gjat\u00eb. Problemi i dyt\u00eb \u00ebsht\u00eb se gabimi mund t\u00eb mos jet\u00eb i zakonsh\u00ebm, dhe disa klikime p\u00ebr t\u00eb m\u00ebsuar m\u00eb tej modelin do t\u00eb ishin t\u00eb pamjaftueshme. Ajo q\u00eb duket si k\u00ebto dy efekte t\u00eb pa lidhura mund t\u00eb kompensohet ndjesh\u00ebm n\u00ebse aplikohet qasja e m\u00ebposhtme.<\/p>\n<ol>\n<li>Krijojm\u00eb nj\u00eb shtr\u00ebnges\u00eb t\u00eb p\u00ebrkohshme. \n<\/li>\n<li>D\u00ebrgojm\u00eb t\u00eb dh\u00ebnat nga ajo n\u00eb model, i cili p\u00ebrdit\u00ebsohet rregullisht, p\u00ebrfshir\u00eb t\u00eb dh\u00ebnat e marra. K\u00ebtu, natyrisht, \u00ebsht\u00eb e r\u00ebnd\u00ebsishme q\u00eb heuristika t\u00eb ket\u00eb nj\u00eb sakt\u00ebsi t\u00eb lart\u00eb, q\u00eb t\u00eb mos ulin cil\u00ebsin\u00eb e t\u00eb dh\u00ebnave n\u00eb grupin e trajnim. \n<\/li>\n<li>Pastaj vendosim monitorim p\u00ebr aktivizimin e shtr\u00ebnges\u00ebs, dhe n\u00ebse pas nj\u00eb periudhe shtr\u00ebngesa nuk aktivizohet m\u00eb dhe mbulohet plot\u00ebsisht nga modeli, at\u00ebher\u00eb mund ta heqim me siguri. Tani kjo problematik\u00eb \u00ebsht\u00eb e pak\u00ebt q\u00eb do t\u00eb p\u00ebrs\u00ebritet.\n<\/li>\n<\/ol>\n<p>\nPra, armata e shtr\u00ebngesave \u00ebsht\u00eb shum\u00eb e dobishme. E r\u00ebnd\u00ebsishme \u00ebsht\u00eb q\u00eb sh\u00ebrbimi i tyre t\u00eb jet\u00eb i p\u00ebrkohsh\u00ebm, dhe jo t\u00eb p\u00ebrhersh\u00ebm. <\/p>\n<h2>M\u00ebsimi m\u00eb tej<\/h2>\n<p>\nRitraining \u00ebsht\u00eb procesi i shtimit t\u00eb t\u00eb dh\u00ebnave t\u00eb reja, t\u00eb marra si rezultat i feedback-ut nga p\u00ebrdoruesit ose nga sisteme t\u00eb tjera, dhe m\u00ebsimi i modelit ekzistues mbi to. Rreth ritrainimit mund t\u00eb ket\u00eb disa probleme:<\/p>\n<ol>\n<li>Modeli mund t\u00eb mos e mb\u00ebshtes\u00eb thjesht ritrainimin dhe t\u00eb m\u00ebsoj\u00eb vet\u00ebm nga fillimi. \n<\/li>\n<li>Nuk ka asgj\u00eb n\u00eb librin e natyr\u00ebs q\u00eb thot\u00eb se ritrainimi do t\u00eb p\u00ebrmir\u00ebsoj\u00eb patjet\u00ebr cil\u00ebsin\u00eb e pun\u00ebs n\u00eb prodhim. Ndodhin shpesh t\u00eb kund\u00ebrtat, pra \u00ebsht\u00eb e mundur vet\u00ebm p\u00ebrkeq\u00ebsimi.\n<\/li>\n<li>Ndryshimet mund t\u00eb jen\u00eb t\u00eb paparashikueshme. Ky \u00ebsht\u00eb nj\u00eb \u00e7\u00ebshtje mjaft delikate q\u00eb ne e kemi identifikuar p\u00ebr vete. Edhe n\u00ebse modeli i ri n\u00eb A\/B testim tregon rezultate t\u00eb ngjashme krahasuar me aktualin, kjo nuk do t\u00eb thot\u00eb aspak se do t\u00eb punoj\u00eb n\u00eb t\u00eb nj\u00ebjt\u00ebn m\u00ebnyr\u00eb. Funksionimi i tyre mund t\u00eb diferencoj\u00eb n\u00eb ndonj\u00eb p\u00ebrqindje q\u00eb mund t\u00eb sjell\u00eb gabime t\u00eb reja ose t\u00eb rikthej\u00eb ato t\u00eb vjetra q\u00eb jan\u00eb korrigjuar. Me gabimet aktuale si ne, ashtu edhe p\u00ebrdoruesit, tashm\u00eb dim\u00eb t\u00eb jetojm\u00eb, dhe kur ndodhin nj\u00eb num\u00ebr i madh i gabimeve t\u00eb reja, p\u00ebrdoruesi gjithashtu mund t\u00eb mos e kuptoj\u00eb se \u00e7far\u00eb po ndodh, sepse ai pret nj\u00eb sjellje t\u00eb parashikueshme.\n<\/li>\n<\/ol>\n<p>\nPrandaj, e r\u00ebnd\u00ebsishme n\u00eb rinovimin e modelit \u00ebsht\u00eb t\u00eb sigurojm\u00eb p\u00ebrmir\u00ebsimin e tij, ose t\u00eb pakt\u00ebn t\u00eb mos e p\u00ebrkeq\u00ebsojm\u00eb at\u00eb. <\/p>\n<p>E para q\u00eb na vjen n\u00eb mendje kur flasim p\u00ebr rinovimin \u00ebsht\u00eb qasja e Aktiv\u00ebs s\u00eb M\u00ebsimit. \u00c7far\u00eb do t\u00eb thot\u00eb kjo? P\u00ebr shembull, nj\u00eb klasifikues p\u00ebrcakton n\u00ebse nj\u00eb let\u00ebr i p\u00ebrket financave, dhe rreth kufijve t\u00eb tij t\u00eb vendosjes s\u00eb vendimeve shtojm\u00eb nj\u00eb most\u00ebr t\u00eb shembujve t\u00eb etiketuar. Kjo funksionon mir\u00eb, p\u00ebr shembull, n\u00eb reklam\u00eb, ku ka shum\u00eb reagime dhe mund t\u00eb m\u00ebsojm\u00eb modelin n\u00eb m\u00ebnyr\u00eb online. Por n\u00ebse ka pak reagime, at\u00ebher\u00eb marrim nj\u00eb most\u00ebr t\u00eb konsiderueshme t\u00eb p\u00ebrjashtuar n\u00eb lidhje me shp\u00ebrndarjen e dh\u00ebnave n\u00eb prodhim, mbi t\u00eb cil\u00ebn nuk mund t\u00eb vler\u00ebsojm\u00eb sjelljen e modelit gjat\u00eb p\u00ebrdorimit.<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/fd1b3e07bfaf896dde3248e43537f61a.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nN\u00eb t\u00eb v\u00ebrtet\u00eb, q\u00ebllimi yn\u00eb \u00ebsht\u00eb t\u00eb ruajm\u00eb modelet e vjetra, t\u00eb njohura p\u00ebr modelin, dhe t\u00eb fitojm\u00eb t\u00eb reja. K\u00ebtu vazhdim\u00ebsia \u00ebsht\u00eb e r\u00ebnd\u00ebsishme. Modeli, t\u00eb cilin shpesh e hedhim me v\u00ebshtir\u00ebsi, tashm\u00eb funksionon, prandaj mund t\u00eb orientohemi mbi performanc\u00ebn e tij. <\/p>\n<p>N\u00eb post\u00eb aplikohen modele t\u00eb ndryshme: pem\u00eb, lineare, rrjete nervore. P\u00ebr \u00e7do nj\u00eb b\u00ebm\u00eb algoritmin ton\u00eb t\u00eb ri-trajnimit. N\u00eb procesin e ri-trajnimit ne jo vet\u00ebm q\u00eb marrim t\u00eb dh\u00ebna t\u00eb reja, por shpesh edhe karakteristika t\u00eb reja, t\u00eb cilat do t\u00eb kemi parasysh n\u00eb t\u00eb gjith\u00eb algoritm\u00ebt posht\u00eb.<\/p>\n<h3>Modelet lineare<\/h3>\n<p>\nLe t\u00eb marrim nj\u00eb regresion logjistik. E formojm\u00eb humbjen e modelit nga komponent\u00ebt e m\u00ebposht\u00ebm:<\/p>\n<ul>\n<li>LogLoss n\u00eb t\u00eb dh\u00ebnat e reja;\n<\/li>\n<li>rregullojm\u00eb peshat e karakteristikave t\u00eb reja (t\u00eb vjetrat nuk i prekim);\n<\/li>\n<li>m\u00ebsojm\u00eb edhe mbi t\u00eb dh\u00ebnat e vjetra p\u00ebr t\u00eb ruajtur modelet e vjetra;\n<\/li>\n<li>dhe, ndoshta, m\u00eb e r\u00ebnd\u00ebsishmja: vendosim Harmonic Regularization, e cila garanton q\u00eb peshat e reja t\u00eb mos ndryshojn\u00eb shum\u00eb n\u00eb raport me modelin e vjet\u00ebr n\u00eb norm\u00eb.\n<\/li>\n<\/ul>\n<p>\nDuke qen\u00eb se \u00e7do komponent\u00eb i humbjes ka koeficient\u00eb, ne mund t\u00eb p\u00ebrcaktojm\u00eb vlerat optimale p\u00ebr detyr\u00ebn ton\u00eb n\u00eb kros-validim ose n\u00eb p\u00ebrputhje me k\u00ebrkesat produktive.<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/00ae86d23afb780f5e260e61f833d4bd.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>Pem\u00ebt<\/h3>\n<p>\nT\u00eb kalojm\u00eb te pem\u00ebt e vendimeve. Ne kemi realizuar algoritmin e m\u00ebposht\u00ebm p\u00ebr ri-trajnim t\u00eb pem\u00ebve:<\/p>\n<ol>\n<li>N\u00eb prodhim punon nj\u00eb pyll me 100\u2014300 pem\u00eb, i cili \u00ebsht\u00eb trajnuar n\u00eb nj\u00eb dataset t\u00eb vjet\u00ebr.\n<\/li>\n<li>Ne eliminojm\u00eb n\u00eb fund M = 5 cop\u00eb dhe shtojm\u00eb 2M = 10 t\u00eb reja, t\u00eb st\u00ebrvitur n\u00eb t\u00eb gjith\u00eb grupin e t\u00eb dh\u00ebnave, por me pesh\u00eb t\u00eb lart\u00eb p\u00ebr t\u00eb dh\u00ebnat e reja, q\u00eb natyrsh\u00ebm garanton nj\u00eb ndryshim inkremental t\u00eb modelit.\n<\/li>\n<\/ol>\n<p>\nSigurisht, q\u00eb me kalimin e koh\u00ebs numri i pem\u00ebve rritet ndjesh\u00ebm, dhe ato duhet t\u00eb reduktohen rregullisht p\u00ebr t'u p\u00ebrshtatur n\u00eb koh\u00eb. P\u00ebr k\u00ebt\u00eb p\u00ebrdorim p\u00ebrhapjen e njohur tani Knowledge Distillation (KD). M\u00eb shkurt p\u00ebr parimin e tij t\u00eb funksionimit.<\/p>\n<ol>\n<li>Ne kemi modelin aktual \"t\u00eb komplikuar\". E nisim at\u00eb n\u00eb grupin e t\u00eb dh\u00ebnave p\u00ebr trajnim dhe marrim shp\u00ebrndarjen e probabiliteteve t\u00eb klasave n\u00eb daljen.\n<\/li>\n<li>M\u00eb pas e m\u00ebsojm\u00eb modelin nx\u00ebn\u00ebs (n\u00eb k\u00ebt\u00eb rast, nj\u00eb model me m\u00eb pak pem\u00eb) t\u00eb p\u00ebrs\u00ebris\u00eb rezultatet e pun\u00ebs s\u00eb modelit, duke p\u00ebrdorur shp\u00ebrndarjen e klasave si variab\u00ebl q\u00ebllimi.\n<\/li>\n<li>\u00cbsht\u00eb e r\u00ebnd\u00ebsishme t\u00eb theksohet se ne nuk p\u00ebrdorim asnj\u00eb m\u00ebnyr\u00eb markup t\u00eb dataset-it dhe ndonj\u00ebher\u00eb mund t\u00eb p\u00ebrdorim t\u00eb dh\u00ebna rast\u00ebsore. Sigurisht, ne p\u00ebrdorim nj\u00eb most\u00ebr t\u00eb dh\u00ebnash nga rrjedha live si nj\u00eb grup trajtimi p\u00ebr modelin e studentit. K\u00ebshtu, grupi trening ofron sakt\u00ebsin\u00eb e modelit, nd\u00ebrsa mostra e rrjedh\u00ebs garanton performanc\u00eb t\u00eb ngjashme n\u00eb shp\u00ebrndarjen e prodhimit, duke kompensuar devijimin e grupit t\u00eb trajtimit.\n<\/li>\n<\/ol>\n<p>\n<img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/3fdfcb9b1e5a6fa824226a429afc475a.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nKombinimi i k\u00ebtyre dy metodave (shtimi i pem\u00ebve dhe reduktimi i rregullt i numrit t\u00eb tyre n\u00ebp\u00ebrmjet Knowledge Distillation) garanton hyrjen e modeleve t\u00eb reja dhe vazhdim\u00ebsin\u00eb e plot\u00eb.<\/p>\n<p>Me KD, gjithashtu b\u00ebjm\u00eb dallimin e operacioneve me karakteristikat e modelit, si\u00e7 \u00ebsht\u00eb heqja e karakteristikave dhe funksionimi mbi mungesat. N\u00eb rastin ton\u00eb, kemi nj\u00eb s\u00ebr\u00eb karakteristikash statistikore t\u00eb r\u00ebnd\u00ebsishme (p\u00ebr d\u00ebrguesit, hash-et tekstore, URL-t\u00eb, etj.), t\u00eb cilat ruhen n\u00eb baz\u00ebn e t\u00eb dh\u00ebnave, duke pasur nj\u00eb substanc\u00eb q\u00eb refuzon. Nj\u00eb zhvillim i till\u00eb \u00ebsht\u00eb, sigurisht, jasht\u00eb p\u00ebrgatitjes s\u00eb modelit, pasi n\u00eb setin e trajnimit nuk paraqiten situata refuzimi. N\u00eb raste t\u00eb tilla, ne kombinojm\u00eb teknikat KD dhe augmentim: gjat\u00eb trajtimit p\u00ebr nj\u00eb pjes\u00eb t\u00eb t\u00eb dh\u00ebnave, ne heqim ose zerojme karakteristikat e nevojshme, nd\u00ebrsa etiketat (daljet e modelit aktual) i marrim origjinale, modeli nx\u00ebn\u00ebs m\u00ebson t\u00eb p\u00ebrs\u00ebris\u00eb k\u00ebt\u00eb shp\u00ebrndarje.<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/5e1f5af9a359646a49f4fe88ffed1025.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nKemi v\u00ebn\u00eb re se sa m\u00eb serioze t\u00eb jet\u00eb manipulimi i modeleve, aq m\u00eb shum\u00eb n\u00eb proporcione k\u00ebrkohet sht\u00ebpia e fluxit.<\/p>\n<p>P\u00ebr t\u00eb hequr shenjat, operacioni m\u00eb i thjesht\u00eb k\u00ebrkon vet\u00ebm nj\u00eb pjes\u00eb t\u00eb vog\u00ebl t\u00eb rrjedh\u00ebs, pasi ndryshohen vet\u00ebm disa shenja dhe modeli aktual ka m\u00ebsuar mbi t\u00eb nj\u00ebjtin set \u2014 diferenca \u00ebsht\u00eb minimale. P\u00ebr t\u00eb thjeshtuar modelin (reduktimin e numrit t\u00eb pem\u00ebve disa her\u00eb), tashm\u00eb k\u00ebrkohen 50 nga 50. Nd\u00ebrsa p\u00ebr t\u00eb humbur statistikat e r\u00ebnd\u00ebsishme q\u00eb ndikojn\u00eb seriozisht n\u00eb performanc\u00ebn e modelit, k\u00ebrkohet m\u00eb shum\u00eb rrjedh\u00eb p\u00ebr t\u00eb balancuar funksionimin e modelit t\u00eb ri q\u00eb \u00ebsht\u00eb i q\u00ebndruesh\u00ebm ndaj humbjeve n\u00eb t\u00eb gjitha llojet e shkresave. <\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/8f3729cfff43be9ecdc47d532daa8d6f.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>FastText<\/h3>\n<p>\nLe t\u00eb kalojm\u00eb te FastText. Kujtojm\u00eb se p\u00ebrfaq\u00ebsimi (Embedding) i fjal\u00ebs p\u00ebrb\u00ebhet nga shuma e embedding-ut t\u00eb vet\u00eb fjal\u00ebs dhe t\u00eb gjitha N-gram e saj me shkronja, zakonisht trigramet. Duke qen\u00eb se mund t\u00eb ket\u00eb mjaft trigrame, p\u00ebrdoret Bucket Hashing, pra transformimi i gjith\u00eb hap\u00ebsir\u00ebs n\u00eb nj\u00eb harta t\u00eb fixuar. Si rezultat, matrica e pesheve rezulton me dimensionin e sloj t\u00eb brendsh\u00ebm mbi numrin e fjal\u00ebve + bakete. <\/p>\n<p>Kur trajnimi prishtisa shfaqen ve\u00e7ori t\u00eb reja: fjal\u00eb dhe trigram\u00eb. N\u00eb trajtimin standard t\u00eb modelit nga Facebook, nuk ndodhin ndryshime t\u00eb r\u00ebnd\u00ebsishme. Trajtohen vet\u00ebm peshat e vjetra me entropi t\u00eb kryq\u00ebzuar mbi t\u00eb dh\u00ebnat e reja. N\u00eb k\u00ebt\u00eb m\u00ebnyr\u00eb, ve\u00e7orit\u00eb e reja nuk p\u00ebrdoren, natyrisht, ky qasje ka t\u00eb gjitha mang\u00ebsit\u00eb e p\u00ebrmendura m\u00eb lart, q\u00eb lidhen me paparashikueshm\u00ebrin\u00eb e modelit n\u00eb prodhim. Prandaj, ne kemi b\u00ebr\u00eb disa p\u00ebrmir\u00ebsime n\u00eb FastText. Shtojm\u00eb t\u00eb gjitha peshat e reja (fjal\u00eb dhe trigram\u00eb), trajtojm\u00eb t\u00eb gjith\u00eb matric\u00ebn me entropi t\u00eb kryq\u00ebzuar dhe shtojm\u00eb rregullimin harmonik n\u00eb p\u00ebrputhje me modelin linear, i cili garanton ndryshime t\u00eb pap\u00ebrfillshme t\u00eb peshave t\u00eb vjetra.<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/4491639587d1c91ac91d77b587ee0111.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>CNN<\/h3>\n<p>\nMe rrjetet konvencionale \u00ebsht\u00eb pak m\u00eb e komplikuar. N\u00ebse n\u00eb CNN trajtohen katet e fundit, natyrisht, mund t\u00eb aplikohet rregullimi harmonik dhe t\u00eb sigurohet trash\u00ebgimia. Por n\u00eb rastin kur k\u00ebrkohet trajtimi i gjith\u00eb rrjetit, at\u00ebher\u00eb nuk mund t\u00eb aplikoni nj\u00eb rregullim t\u00eb till\u00eb n\u00eb t\u00eb gjitha katet. Megjithat\u00eb, ka nj\u00eb mund\u00ebsi me trajnimin e embedding-eve komplementar\u00eb p\u00ebrmes Triplet Loss (<noindex><a rel=\"nofollow\" href=\"https:\/\/arxiv.org\/abs\/1503.03832\">artiku origjinal<\/a><\/noindex>).<\/p>\n<h4>Triplet Loss<\/h4>\n<p>\nN\u00eb shembullin e detyr\u00ebs s\u00eb anti-fishing do t\u00eb shqyrtojm\u00eb n\u00eb p\u00ebrgjith\u00ebsi Triplet Loss. Marrim logon ton\u00eb, si dhe shembuj pozitiv\u00eb dhe negativ\u00eb t\u00eb logove t\u00eb kompanive t\u00eb tjera. Minimizohet distanca mes t\u00eb par\u00ebve dhe maksimalizohet distanca mes t\u00eb dyt\u00ebve, duke e b\u00ebr\u00eb k\u00ebt\u00eb me nj\u00eb toleranc\u00eb t\u00eb vog\u00ebl p\u00ebr t\u00eb siguruar nj\u00eb kompakt\u00ebsi m\u00eb t\u00eb madhe t\u00eb grupeve. <\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/dc8547fa042286798eb5c2f0887c4da6.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nN\u00ebse e rip\u00ebrvojm\u00eb rrjetin, hap\u00ebsira e matjes ndryshon plot\u00ebsisht, dhe b\u00ebhet krejt\u00ebsisht e papajtueshme me t\u00eb m\u00ebparshmen. Kjo \u00ebsht\u00eb nj\u00eb problem serioz n\u00eb detyrat q\u00eb p\u00ebrdorin vektor\u00ebt. P\u00ebr ta anashkaluar k\u00ebt\u00eb problem, do t\u00eb p\u00ebrziejm\u00eb nd\u00ebrsa m\u00ebsojm\u00eb embeddimet e vjetra.<\/p>\n<p>Ne kemi shtuar t\u00eb dh\u00ebna t\u00eb reja n\u00eb setin e trajnimit dhe po m\u00ebsojm\u00eb nga zero versionin e dyt\u00eb t\u00eb modelit. N\u00eb faz\u00ebn e dyt\u00eb, ne rip\u00ebrvojm\u00eb rrjetin ton\u00eb (Finetuning): fillimisht, sa m\u00eb shum\u00eb q\u00eb p\u00ebrmir\u00ebsohet, vet\u00ebm shtresa e fundit, pastaj zgjidhet e gjith\u00eb rrjeta. N\u00eb procesin e krijimit t\u00eb trejve, vet\u00ebm nj\u00eb pjes\u00eb e embeddimeve llogaritet me modelin e m\u00ebsuar, pjesa tjet\u00ebr me t\u00eb vjetrin. K\u00ebshtu, gjat\u00eb rip\u00ebrvoj\u00ebs sigurojm\u00eb pajtueshm\u00ebrin\u00eb e hap\u00ebsirave t\u00eb matjes v1 dhe v2. Nj\u00eb version i ve\u00e7ant\u00eb i regularizimit harmonik.<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/854d4fcc97775b24e6863cc38743cd27.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<\/p>\n<h3>Arkitektura e plot\u00eb<\/h3>\n<p>\nN\u00ebse e shqyrtojm\u00eb sistemin si nj\u00eb t\u00ebr\u00eb p\u00ebr shembullin e anti-spamit, at\u00ebher\u00eb modelet nuk jan\u00eb t\u00eb izoluara, por jan\u00eb t\u00eb shtrira nj\u00ebra brenda tjetr\u00ebs. Marrim figura, tekst dhe karakteristika t\u00eb tjera, duke p\u00ebrdorur CNN dhe Fast Text p\u00ebr t\u00eb marr\u00eb embedding. M\u00eb pas, mbi embedding aplikohet klasifikuesit, t\u00eb cil\u00ebt japin skora p\u00ebr klasa t\u00eb ndryshme (llojet e mesazheve, spam, prania e logos). Skor\u00ebt dhe karakteristikat tashm\u00eb n\u00eb pik\u00ebn e vendimmarrjes p\u00ebrmes nj\u00eb pylli drur\u00ebsh. Klasifikuesit e ndar\u00eb n\u00eb k\u00ebt\u00eb skem\u00eb lejojn\u00eb nj\u00eb interpretim m\u00eb t\u00eb mir\u00eb t\u00eb rezultateve t\u00eb sistemit dhe t\u00eb p\u00ebrmir\u00ebsojn\u00eb m\u00eb sakt\u00eb komponent\u00ebt n\u00eb rast se shfaqen probleme, sesa t'i dor\u00ebzojm\u00eb t\u00eb dh\u00ebnat e plota n\u00eb drunjt\u00eb e vendimmarrjes n\u00eb form\u00ebn e pap\u00ebrpunuar.<\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/30effc5ef7398db5f085b6dd415647d3.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nSi rezultat, ne garantojm\u00eb pasardh\u00ebs t\u00eb \u00e7do niveli. N\u00eb nivelin e posht\u00ebm n\u00eb CNN dhe Fast Text p\u00ebrdorim rregullimin harmonik, p\u00ebr klasifikuesit n\u00eb mes \u2014 gjithashtu rregullimin harmonik dhe kalibrimin e skores p\u00ebr p\u00ebrputhshm\u00ebrin\u00eb e shp\u00ebrndarjes s\u00eb probabilitetit. Nd\u00ebrsa boostimi i drur\u00ebve m\u00ebsohet n\u00eb m\u00ebnyr\u00eb incrementale ose p\u00ebrmes Knowledge Distillation.<\/p>\n<p>N\u00eb p\u00ebrgjith\u00ebsi, mb\u00ebshtetje p\u00ebr nj\u00eb sistem t\u00eb till\u00eb t\u00eb thelluar t\u00eb m\u00ebsimit t\u00eb makinave zakonisht paraqet v\u00ebshtir\u00ebsi, pasi \u00e7do komponent n\u00eb nivelin m\u00eb t\u00eb ul\u00ebt \u00e7on n\u00eb p\u00ebrdit\u00ebsimin e krejt sistemit m\u00eb lart. Por duke qen\u00eb se n\u00eb konfigurimin ton\u00eb \u00e7do komponent ndryshon pak dhe \u00ebsht\u00eb i pajtuesh\u00ebm me at\u00eb t\u00eb m\u00ebparsh\u00ebm, e gjith\u00eb sistemi mund t\u00eb p\u00ebrdit\u00ebsohet n\u00eb pjes\u00eb pa pasur nevoj\u00eb t\u00eb ritrainojm\u00eb t\u00ebr\u00eb struktur\u00ebn, \u00e7ka lejon mb\u00ebshtetje pa ndonj\u00eb ngarkes\u00eb serioze. <\/p>\n<h2>D\u00ebrgo<\/h2>\n<p>\nKemi shqyrtuar mbledhjen e t\u00eb dh\u00ebnave dhe ritrainimin e llojeve t\u00eb ndryshme t\u00eb modeleve, prandaj po kalojm\u00eb n\u00eb d\u00ebrgimin e tyre n\u00eb ambientin e prodhimit.<\/p>\n<h3>Testimi A\/B<\/h3>\n<p>\nSi e thash\u00eb m\u00eb par\u00eb, gjat\u00eb procesit t\u00eb mbledhjes s\u00eb t\u00eb dh\u00ebnave, zakonisht marrim nj\u00eb most\u00ebr me devijim, e cila nuk lejon t\u00eb vler\u00ebsojm\u00eb performanc\u00ebn prodhuese t\u00eb modelit. Prandaj, kur zbatohet modelin, \u00ebsht\u00eb e domosdoshme t\u00eb krahasohet me versionin e m\u00ebparsh\u00ebm, p\u00ebr t\u00eb kuptuar se si po shkojn\u00eb pun\u00ebt n\u00eb t\u00eb v\u00ebrtet\u00eb, pra duhet t\u00eb kryhen testet A\/B. N\u00eb fakt, procesi i p\u00ebrgatitjes dhe analiz\u00ebs s\u00eb shifrave \u00ebsht\u00eb mjaft rutin\u00eb dhe i p\u00ebrshtatet automatikisht. Ne ia dalim t\u00eb nxjerrim modelet tona gradualisht n\u00eb 5%, 30%, 50% dhe 100% t\u00eb p\u00ebrdoruesve, duke mbledhur t\u00eb gjitha metrikat e disponueshme p\u00ebr p\u00ebrgjigjet e modelit dhe feedback-un e p\u00ebrdoruesve. N\u00eb rast t\u00eb ndonj\u00eb devijimi t\u00eb r\u00ebnd\u00ebsish\u00ebm, ne automatikisht e p\u00ebrshtasim modelin, nd\u00ebrsa p\u00ebr rastet e tjera, pasi t\u00eb kemi marr\u00eb nj\u00eb num\u00ebr t\u00eb mjaftuesh\u00ebm klikimesh nga p\u00ebrdoruesit, marrim vendimin p\u00ebr rritjen e p\u00ebrqindjes. Si p\u00ebrfundim, e \u00e7ojm\u00eb modelin e ri deri n\u00eb 50% t\u00eb p\u00ebrdoruesve plot\u00ebsisht automatikisht, nd\u00ebrsa shp\u00ebrndarjen p\u00ebr t\u00eb gjith\u00eb audienc\u00ebn e miraton nj\u00eb njeri, edhe pse ky hap mund t\u00eb automatizohet gjithashtu.<\/p>\n<p>Megjithat\u00eb, proceset e testimeve A\/B ofrojn\u00eb mund\u00ebsi p\u00ebr optimizim. Problemi \u00ebsht\u00eb se \u00e7do test A\/B zgjat mjaft gjat\u00eb (n\u00eb rastin ton\u00eb, ai zgjat nga 6 deri n\u00eb 24 or\u00eb n\u00eb var\u00ebsi t\u00eb sasis\u00eb s\u00eb feedback-ut), duke e b\u00ebr\u00eb at\u00eb mjaft t\u00eb shtrenjt\u00eb dhe me burime t\u00eb kufizuara. P\u00ebrve\u00e7 k\u00ebsaj, k\u00ebrkohet nj\u00eb p\u00ebrqindje mjaft e lart\u00eb e fluksit p\u00ebr testin, p\u00ebr t\u00eb p\u00ebrshpejtuar koh\u00ebn totale t\u00eb testit A\/B (t\u00eb grumbullosh nj\u00eb most\u00ebr statistikisht dometh\u00ebn\u00ebse p\u00ebr vler\u00ebsimin e matjeve n\u00eb nj\u00eb p\u00ebrqindje t\u00eb vog\u00ebl mund t\u00eb zgjas\u00eb shum\u00eb), q\u00eb e b\u00ebn numrin e vendeve p\u00ebr A\/B teste jasht\u00ebzakonisht t\u00eb kufizuar. \u00cbsht\u00eb e qart\u00eb se na nevojitet t\u00eb nxjerrim n\u00eb test vet\u00ebm modelet m\u00eb premtuese, t\u00eb cilat gjat\u00eb procesit t\u00eb ristrukturohen ne merrim mjaft shum\u00eb.<\/p>\n<p>P\u00ebr t\u00eb zgjidhur k\u00ebt\u00eb problem, ne trajnuam nj\u00eb klasifikues t\u00eb ve\u00e7ant\u00eb, i cili parashikon suksesin e testit A\/B. P\u00ebr k\u00ebt\u00eb, si karakteristika marrim statistik\u00ebn e marrjes s\u00eb vendimeve, Precizionin, Rrethin\u00eb dhe metrika t\u00eb tjera mbi grupin m\u00ebsues, mbi at\u00eb t\u00eb mbajtur dhe mbi nj\u00eb most\u00ebr nga fluksi. Gjithashtu krahasojm\u00eb modelin me at\u00eb aktual n\u00eb prodhim, me heuristik\u00ebt dhe marrim parasysh kompleksitetin e modelit. Duke p\u00ebrdorur t\u00eb gjitha k\u00ebto karakteristika, klasifikuesi i trajnuar mbi historin\u00eb e testeve vler\u00ebson kandidat\u00ebt e modeleve, n\u00eb rastin ton\u00eb k\u00ebto jan\u00eb pyjet e pem\u00ebve, dhe merr nj\u00eb vendim se cilin prej tyre ta d\u00ebrgoj\u00eb n\u00eb testin A\/B. <\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/41b121987209a663075be399e83f5a50.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nN\u00eb momentin e implementimit, ky qasje lejoj q\u00eb numri i testeve A\/B t\u00eb suksesshme t\u00eb rritet disa her\u00eb.<\/p>\n<h3>Testimi &amp; monitorimi<\/h3>\n<p>\nTestimi dhe monitorimi, si\u00e7 tregon \u00e7udi, nuk d\u00ebmtojn\u00eb sh\u00ebndetin ton\u00eb, p\u00ebrkundrazi, p\u00ebrmir\u00ebsojn\u00eb dhe na p\u00ebrshpejtojn\u00eb nga streset e tepruara. Testimi mund t\u00eb parandaloj\u00eb d\u00ebshtimin, nd\u00ebrsa monitorimi \u2014 ta zbuloj\u00eb at\u00eb n\u00eb koh\u00eb, p\u00ebr t\u00eb reduktuar ndikimin mbi p\u00ebrdoruesit.<\/p>\n<p>\u00cbsht\u00eb e r\u00ebnd\u00ebsishme t\u00eb kuptoni se her\u00ebt a von\u00eb sistemi juaj gjithmon\u00eb do t\u00eb gaboj\u00eb \u2014 kjo \u00ebsht\u00eb e lidhur me ciklin e zhvillimit t\u00eb \u00e7do programi. N\u00eb fillim t\u00eb zhvillimit t\u00eb sistemit ka shum\u00eb defekte derisa gjith\u00e7ka t\u00eb stabilizohet dhe t\u00eb p\u00ebrfundoj\u00eb faza kryesore e risive. Por me kalimin e koh\u00ebs, entropia b\u00ebn t\u00eb vet\u00ebn, dhe gabimet shfaqen p\u00ebrs\u00ebri \u2014 p\u00ebr shkak t\u00eb degradimit t\u00eb komponent\u00ebve dhe ndryshimeve n\u00eb t\u00eb dh\u00ebna, si\u00e7 e thash\u00eb m\u00eb par\u00eb.<\/p>\n<p>K\u00ebtu do t\u00eb doja t\u00eb theksoja se \u00e7do sistem t\u00eb m\u00ebsuarit t\u00eb makineris\u00eb duhet t\u00eb shihet nga k\u00ebndv\u00ebshtrimi i p\u00ebrfitimit t\u00eb tij gjat\u00eb gjith\u00eb ciklit t\u00eb jet\u00ebs. M\u00eb posht\u00eb n\u00eb grafik \u00ebsht\u00eb nj\u00eb shembull i funksionimit t\u00eb sistemit p\u00ebr kapjen e nj\u00eb specie t\u00eb rrall\u00eb t\u00eb spamit (n\u00eb grafik linja \u00ebsht\u00eb af\u00ebr zeros). Nj\u00ebher\u00eb p\u00ebr shkak t\u00eb nj\u00eb karakteristike t\u00eb gabuar t\u00eb t\u00eb dh\u00ebnave, ai u \u00e7mend. Me fat, nuk kishte monitorim p\u00ebr p\u00ebrdorimin anormal, si rezultat sistemi filloi t\u00eb ruante email-e n\u00eb dosjen \"spam\" n\u00eb kufirin e vendimit n\u00eb nj\u00eb sasi t\u00eb madhe. Pavar\u00ebsisht nga rregullimi i pasojave, sistemi tashm\u00eb kishte gabuar aq shum\u00eb her\u00eb sa nuk do ta shlyej\u00eb kurr\u00eb kostot as p\u00ebr pes\u00eb vjet. Kjo \u00ebsht\u00eb nj\u00eb d\u00ebshtim total nga k\u00ebndv\u00ebshtrimi i ciklit t\u00eb jet\u00ebs s\u00eb modelit. <\/p>\n<p><img decoding=\"async\" alt=\"Zbatimi i m\u00ebsimit t\u00eb makineris\u00eb n\u00eb Mail.ru\" src=\"\/wp-content\/uploads\/2019\/11\/65efd799537ceea3761cabc3a764ba90.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nPrandaj, nj\u00eb gj\u00eb kaq e thjesht\u00eb si monitorimi mund t\u00eb b\u00ebhet thelb\u00ebsore n\u00eb jet\u00ebn e modelit. P\u00ebrve\u00e7 metrikeve standarde dhe t\u00eb dukshme, ne shqyrtojm\u00eb shp\u00ebrndarjen e p\u00ebrgjigjeve dhe skor\u00ebve t\u00eb modelit, si dhe shp\u00ebrndarjen e vlerave t\u00eb ve\u00e7orive ky\u00e7e. Me an\u00eb t\u00eb divergenc\u00ebs KL, ne mund t\u00eb krahasojm\u00eb shp\u00ebrndarjen aktuale me at\u00eb historike ose vlerat n\u00eb testimin A\/B me rrjedh\u00ebn tjet\u00ebr, q\u00eb na lejon t\u00eb v\u00ebrejm\u00eb anomalit\u00eb n\u00eb model dhe t\u00eb kthehemi mbrapa me ndryshimet n\u00eb koh\u00eb.<\/p>\n<p>N\u00eb shumic\u00ebn e rasteve, ne i lan\u00e7ojm\u00eb versionet tona t\u00eb para t\u00eb sistemeve me an\u00eb t\u00eb heuristikave t\u00eb thjeshta ose modeleve, t\u00eb cilat m\u00eb von\u00eb i p\u00ebrdorim p\u00ebr monitorim. P\u00ebr shembull, ne monitorojm\u00eb modelin NER n\u00eb krahasim me regex p\u00ebr dyqane specifike online, dhe n\u00ebse mbulimi i klasifikuesit bie n\u00eb krahasim me ato, at\u00ebher\u00eb shqyrtojm\u00eb arsyet. Nj\u00eb tjet\u00ebr p\u00ebrdorim i dobish\u00ebm i heuristikave!<\/p>\n<h2>P\u00ebrfundimet<\/h2>\n<p>\nT\u00eb kalojm\u00eb p\u00ebrs\u00ebri p\u00ebrmes mendimeve kryesore t\u00eb artikullit.<\/p>\n<ul>\n<li><b>Fibd\u00e6k<\/b>. Gjithmon\u00eb mendojm\u00eb p\u00ebr p\u00ebrdoruesin: si do t\u00eb jetoj\u00eb ai me gabimet tona dhe si do t\u2019i raportoj\u00eb ato. Nuk harrojm\u00eb se p\u00ebrdoruesit nuk jan\u00eb nj\u00eb burim i past\u00ebr feedback-u p\u00ebr t\u00eb m\u00ebsuar modelet, dhe ky feedback duhet t\u00eb filtrohet me ndihm\u00ebn e sistemeve t\u00eb asistuara ML. N\u00ebse nuk ka mund\u00ebsi p\u00ebr t\u00eb mbledhur sinjale nga p\u00ebrdoruesi, ne k\u00ebrkojm\u00eb burime alternative feedback-u, si sistemet e lidhura. \n<\/li>\n<li><b>M\u00ebsimi m\u00eb tej<\/b>. K\u00ebtu kryesorja \u00ebsht\u00eb kontinuiteti, prandaj mb\u00ebshtetemi n\u00eb modelin aktual n\u00eb prodhim. Modelet e reja i trajnojm\u00eb n\u00eb m\u00ebnyr\u00eb q\u00eb t\u00eb mos diferencohen shum\u00eb nga t\u00eb parat p\u00ebrmes rregullimit harmonik dhe trukeve t\u00eb ngjashme.<\/li>\n<li><b>D\u00ebrgo<\/b>. Autodeploy sipas metrikave redukton ndjesh\u00ebm koh\u00ebn p\u00ebr implementimin e modeleve. Monitorimi i statistikave dhe shp\u00ebrndarjes s\u00eb vendimeve, numri i d\u00ebshtimeve nga p\u00ebrdoruesit \u00ebsht\u00eb i domosdosh\u00ebm p\u00ebr gjumin tuaj t\u00eb qet\u00eb dhe fundjav\u00ebt produktive.\n<\/li>\n<\/ul>\n<p>\nShpresoj q\u00eb ai q\u00eb keni lexuar do t'ju ndihmoj\u00eb t\u00eb p\u00ebrmir\u00ebsoni m\u00eb shpejt sistemet tuaja ML, t'i nxirrni ato n\u00eb treg m\u00eb shpejt dhe t'i b\u00ebni ato m\u00eb t\u00eb besueshme, duke zvog\u00ebluar stresin nga puna.<br \/>\n<br \/>Burimi: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/mailru\/blog\/476714\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u041f\u043e \u043c\u043e\u0442\u0438\u0432\u0430\u043c \u043c\u043e\u0438\u0445 \u0432\u044b\u0441\u0442\u0443\u043f\u043b\u0435\u043d\u0438\u0439 \u043d\u0430 Highload++ \u0438 DataFest Minsk 2019 \u0433. \u0414\u043b\u044f \u043c\u043d\u043e\u0433\u0438\u0445 \u0441\u0435\u0433\u043e\u0434\u043d\u044f \u043f\u043e\u0447\u0442\u0430 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043d\u0435\u043e\u0442\u044a\u0435\u043c\u043b\u0435\u043c\u043e\u0439 \u0447\u0430\u0441\u0442\u044c\u044e \u0436\u0438\u0437\u043d\u0438 \u0432 \u0441\u0435\u0442\u0438. \u0421 \u0435\u0435 \u043f\u043e\u043c\u043e\u0449\u044c\u044e \u043c\u044b \u0432\u0435\u0434\u0435\u043c \u0431\u0438\u0437\u043d\u0435\u0441-\u043f\u0435\u0440\u0435\u043f\u0438\u0441\u043a\u0443, \u0445\u0440\u0430\u043d\u0438\u043c \u0432\u0441\u0435\u0432\u043e\u0437\u043c\u043e\u0436\u043d\u0443\u044e \u0432\u0430\u0436\u043d\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e, \u0441\u0432\u044f\u0437\u0430\u043d\u043d\u0443\u044e \u0441 \u0444\u0438\u043d\u0430\u043d\u0441\u0430\u043c\u0438, \u0431\u0440\u043e\u043d\u0438\u0440\u043e\u0432\u0430\u043d\u0438\u0435\u043c \u043e\u0442\u0435\u043b\u0435\u0439, \u043e\u0444\u043e\u0440\u043c\u043b\u0435\u043d\u0438\u0435\u043c \u0437\u0430\u043a\u0430\u0437\u043e\u0432 \u0438 \u043c\u043d\u043e\u0433\u0438\u043c \u0434\u0440\u0443\u0433\u0438\u043c. \u0412 \u0441\u0435\u0440\u0435\u0434\u0438\u043d\u0435 2018 \u0433\u043e\u0434\u0430 \u043c\u044b \u0441\u0444\u043e\u0440\u043c\u0443\u043b\u0438\u0440\u043e\u0432\u0430\u043b\u0438 \u043f\u0440\u043e\u0434\u0443\u043a\u0442\u043e\u0432\u0443\u044e \u0441\u0442\u0440\u0430\u0442\u0435\u0433\u0438\u044e \u0440\u0430\u0437\u0432\u0438\u0442\u0438\u044f \u043f\u043e\u0447\u0442\u044b. \u041a\u0430\u043a\u043e\u0439 \u0436\u0435 \u0434\u043e\u043b\u0436\u043d\u0430 \u0431\u044b\u0442\u044c [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-53143","post","type-post","status-publish","format-standard","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 4.9.10 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u041f\u043e \u043c\u043e\u0442\u0438\u0432\u0430\u043c \u043c\u043e\u0438\u0445 \u0432\u044b\u0441\u0442\u0443\u043f\u043b\u0435\u043d\u0438\u0439 \u043d\u0430 Highload++ \u0438 DataFest Minsk 2019 \u0433. \u0414\u043b\u044f \u043c\u043d\u043e\u0433\u0438\u0445 \u0441\u0435\u0433\u043e\u0434\u043d\u044f \u043f\u043e\u0447\u0442\u0430 \u044f\u0432\u043b\u044f\u0435\u0442\u0441\u044f \u043d\u0435\u043e\u0442\u044a\u0435\u043c\u043b\u0435\u043c\u043e\u0439 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