{"id":39276,"date":"2019-10-31T22:29:10","date_gmt":"2019-10-31T19:29:10","guid":{"rendered":"https:\/\/prohoster.info\/blog\/proekt-open-data-hub-otkrytaya-platforma-mashinnogo-obucheniya-na-baze-red-hat-openshift\/"},"modified":"2019-10-31T22:29:10","modified_gmt":"2019-10-31T19:29:10","slug":"proekt-open-data-hub-otkrytaya-platforma-mashinnogo-obucheniya-na-baze-red-hat-openshift","status":"publish","type":"post","link":"https:\/\/prohoster.info\/ro\/blog\/administrirovanie\/proekt-open-data-hub-otkrytaya-platforma-mashinnogo-obucheniya-na-baze-red-hat-openshift","title":{"rendered":"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Viitorul a sosit, tehnologiile inteligen\u021bei artificiale \u0219i \u00eenv\u0103\u021b\u0103rii automate sunt deja folosite cu succes de magazinele favorite, companiile de transport \u0219i chiar fermele care cresc curcani. <\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/ac77ba2d00012d4f3bee803c9c26bc9f.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n <br \/>\n\u0218i dac\u0103 ceva exist\u0103, \u00eenseamn\u0103 c\u0103 exist\u0103 deja despre acest lucru pe internet... un proiect deschis! Uita\u021bi-v\u0103 cum Open Data Hub ajut\u0103 la scalarea noilor tehnologii \u0219i la evitarea dificult\u0103\u021bilor \u00een implementarea lor.<br \/>\n<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><br \/>\nCu toate avantajele inteligen\u021bei artificiale (artificial intelligence, AI) \u0219i \u00eenv\u0103\u021b\u0103rii automate (machine learning, ML), organiza\u021biile se confrunt\u0103 adesea cu dificult\u0103\u021bi \u00een scalarea acestor tehnologii. Problemele principale sunt, de obicei, urm\u0103toarele:<\/p>\n<ul>\n<li><b>Schimbul de informa\u021bii \u0219i colaborarea<\/b> \u2013 schimbul de informa\u021bii f\u0103r\u0103 eforturi suplimentare \u0219i colaborarea \u00een regim de itera\u021bii rapide sunt practic imposibile.<\/li>\n<li><b>Accesul la date<\/b> \u2013 pentru fiecare sarcin\u0103 trebuie s\u0103 fie construit din nou \u0219i manual, ceea ce consum\u0103 mult timp.<\/li>\n<li><b>Accesul la cerere<\/b> \u2013 nu exist\u0103 posibilitatea de a ob\u021bine acces la cerere la instrumentele \u0219i platforma de \u00eenv\u0103\u021bare automat\u0103, precum \u0219i la infrastructura de calcul.<\/li>\n<li><b>Produc\u021bia<\/b> \u2013 modelele r\u0103m\u00e2n \u00een etapa de prototip \u0219i nu sunt aduse la exploatarea comercial\u0103.<\/li>\n<li><b>Urm\u0103rirea \u0219i explicarea rezultatelor AI<\/b> \u2013 reproducerea, urm\u0103rirea \u0219i explicarea rezultatelor AI\/ML sunt dificile.<\/li>\n<\/ul>\n<p>\nL\u0103sate nerezolvate, aceste probleme afecteaz\u0103 negativ viteza, eficien\u021ba \u0219i productivitatea profesional\u0103 a speciali\u0219tilor valoro\u0219i \u00een procesarea \u0219i analiza datelor. Acest lucru duce la frustrare, dezam\u0103gire \u00een munc\u0103 \u0219i, \u00een final, a\u0219tept\u0103rile afacerii cu privire la AI\/ML se pierd.<\/p>\n<p>Responsabilitatea pentru rezolvarea acestor probleme revine speciali\u0219tilor IT, care trebuie s\u0103 ofere analistilor de date \u2013 da, ceva asem\u0103n\u0103tor cu un cloud. Dac\u0103 extindem subiectul, este nevoie de o platform\u0103 care ofer\u0103 libertate de alegere \u0219i acces facil, simplu. \u00cen acela\u0219i timp, aceasta ar trebui s\u0103 fie rapid\u0103, u\u0219or reproiectabil\u0103, scalabil\u0103 la cerere \u0219i rezistent\u0103 la erori. Construirea unei astfel de platforme pe baza tehnologiilor cu surs\u0103 deschis\u0103 ajut\u0103 la evitarea dependen\u021bei de furnizori \u0219i la men\u021binerea unui avantaj strategic pe termen lung \u00een ceea ce prive\u0219te controlul costurilor.<\/p>\n<p>Acum c\u00e2\u021biva ani, ceva similar s-a \u00eent\u00e2mplat \u00een dezvoltarea aplica\u021biilor, duc\u00e2nd la apari\u021bia microserviciilor, a mediilor cloud hibride, a automatiz\u0103rii IT \u0219i a proceselor agile. Pentru a gestiona totul, speciali\u0219tii IT au \u00eenceput s\u0103 foloseasc\u0103 containere, Kubernetes \u0219i cloud-uri hibride deschise.<\/p>\n<p>Acum, aceast\u0103 experien\u021b\u0103 este aplicat\u0103 pentru a r\u0103spunde provoc\u0103rilor AI. Astfel, speciali\u0219tii IT creeaz\u0103 platforme care se bazeaz\u0103 pe containere, permit dezvoltarea serviciilor AI\/ML \u00een cadrul proceselor agile, accelereaz\u0103 inova\u021biile \u0219i sunt construite av\u00e2nd \u00een vedere cloud-ul hibrid.<\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/a899093183092edd8b2902f47b0495a8.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p>Construirea unei astfel de platforme va \u00eencepe cu Red Hat OpenShift, platforma noastr\u0103 Kubernetes pentru cloud hibrid, care dispune de un ecosistem \u00een rapid\u0103 expansiune de solu\u021bii software \u0219i hardware pentru ML (NVIDIA, H2O.ai, Starburst, PerceptiLabs etc.). Anumi\u021bi clien\u021bi Red Hat, cum ar fi BMW Group, ExxonMobil \u0219i altele, au desf\u0103\u0219urat deja lan\u021buri de instrumente ML \u0219i procese DevOps bazate pe aceast\u0103 platform\u0103 \u0219i ecosistemul s\u0103u, pentru a duce arhitecturile lor ML \u00een modul de operare industrial \u0219i a accelera activitatea analistilor de date.<\/p>\n<p>Un alt motiv pentru care am lansat proiectul Open Data Hub este de a demonstra un exemplu de arhitectur\u0103 bazat\u0103 pe mai multe proiecte open-source \u0219i a ar\u0103ta cum se poate realiza \u00eentregul ciclu de via\u021b\u0103 al unei solu\u021bii ML pe baza platformei OpenShift.<\/p>\n<h3>Proiectul Open Data Hub<\/h3>\n<p>\nEste un proiect open-source care se dezvolt\u0103 \u00een cadrul unei comunit\u0103\u021bi de dezvoltare corespunz\u0103toare \u0219i realizeaz\u0103 \u00eentregul ciclu de opera\u021biuni \u2013 de la \u00eenc\u0103rcarea \u0219i transformarea datelor ini\u021biale p\u00e2n\u0103 la formarea, antrenarea \u0219i \u00eentre\u021binerea modelului \u2013 \u00een solu\u021bionarea sarcinilor AI\/ML folosind containere \u0219i Kubernetes pe platforma OpenShift. Acest proiect poate fi considerat o implementare de referin\u021b\u0103, un exemplu despre cum s\u0103 construie\u0219ti o solu\u021bie deschis\u0103 de tip \u00abAI\/ML ca serviciu\u00bb bazat\u0103 pe OpenShift \u0219i instrumente open-source corespunz\u0103toare, precum Tensorflow, JupyterHub, Spark \u0219i altele. Este important de men\u021bionat c\u0103 Red Hat utilizeaz\u0103 acest proiect pentru a-\u0219i oferi serviciile AI\/ML. \u00cen plus, OpenShift se integreaz\u0103 cu solu\u021bii software \u0219i hardware ML cheie de la NVIDIA, Seldon, Starbust \u0219i al\u021bi furnizori, facilit\u00e2nd construc\u021bia \u0219i lansarea propriilor sisteme de \u00eenv\u0103\u021bare automat\u0103.<\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/85a4266250e1afca62bdc9406422510c.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p>Proiectul Open Data Hub este destinat urm\u0103toarelor categorii de utilizatori \u0219i scenarii de utilizare:<\/p>\n<ul>\n<li>Analist de date care are nevoie de o solu\u021bie pentru implementarea proiectelor ML, organizat\u0103 pe un model cloud cu func\u021bii de autoservire.<\/li>\n<li>Analist de date care are nevoie de cea mai mare varietate dintr-o gam\u0103 larg\u0103 de instrumente \u0219i platforme AI\/ML open-source de ultim\u0103 genera\u021bie.<\/li>\n<li>Analist de date care are nevoie de acces la surse de date \u00een timpul antren\u0103rii modelelor.<\/li>\n<li>Analist de date care are nevoie de acces la resurse computa\u021bionale (CPU, GPU, memorie).<\/li>\n<li>Analist de date care necesit\u0103 posibilitatea de a colabora \u0219i de a \u00eemp\u0103rt\u0103\u0219i rezultatele muncii cu colegii, primind feedback \u0219i introduc\u00e2nd \u00eembun\u0103t\u0103\u021biri prin itera\u021bii rapide.<\/li>\n<li>Analist de date care dore\u0219te s\u0103 interac\u021bioneze cu dezvoltatorii (\u0219i echipele devops) pentru ca modelele \u0219i rezultatele sale ML s\u0103 ajung\u0103 \u00een produc\u021bie.<\/li>\n<li>Inginer de date care trebuie s\u0103 ofere analistului de date acces la diverse surse de date, respect\u00e2nd normele \u0219i cerin\u021bele de securitate.<\/li>\n<li>Administrator\/operator de sisteme IT care necesit\u0103 capacitatea de a controla f\u0103r\u0103 efort ciclul de via\u021b\u0103 (instalare, configurare, actualizare) al componentelor \u0219i tehnologiilor open-source. De asemenea, sunt necesare instrumentele adecvate de management \u0219i alocare.<\/li>\n<\/ul>\n<p>\nProiectul Open Data Hub reune\u0219te o gam\u0103 larg\u0103 de instrumente open-source pentru realizarea ciclului complet al opera\u021biunilor AI\/ML. Ca instrument principal de lucru pentru analistul de date, se utilizeaz\u0103 Jupyter Notebook. Acest instrument este ast\u0103zi foarte popular printre speciali\u0219tii \u00een procesarea \u0219i analiza datelor, iar Open Data Hub le permite s\u0103 creeze \u0219i s\u0103 gestioneze cu u\u0219urin\u021b\u0103 spa\u021biile de lucru Jupyter Notebook, folosind JupyterHub \u00eencorporat. Pe l\u00e2ng\u0103 crearea \u0219i importul de notebooks Jupyter, proiectul Open Data Hub con\u021bine, de asemenea, o serie de notebooks deja gata, sub form\u0103 de bibliotec\u0103 AI Library.<\/p>\n<p>Aceast\u0103 bibliotec\u0103 reprezint\u0103 o colec\u021bie de componente open-source pentru \u00eenv\u0103\u021barea automat\u0103 \u0219i solu\u021bii pentru scenarii standard, care simplific\u0103 prototiparea rapid\u0103. JupyterHub este integrat cu modelul de acces RBAC al OpenShift, permi\u021b\u00e2nd utilizarea conturilor OpenShift existente \u0219i implementarea unui sistem de autentificare unificat\u0103. \u00cen plus, JupyterHub ofer\u0103 o interfa\u021b\u0103 prietenoas\u0103 pentru utilizatori numit\u0103 spawner, cu ajutorul c\u0103reia utilizatorul poate configura cu u\u0219urin\u021b\u0103 volumul de resurse computa\u021bionale (nuclee de procesor, memorie, GPU) pentru Jupyter Notebook-ul ales.<\/p>\n<p>Dup\u0103 ce analistul de date creeaz\u0103 \u0219i configureaz\u0103 notebook-ul, toate celelalte sarcini sunt preluate de planificatorul Kubernetes, care face parte din OpenShift. Utilizatorii trebuie doar s\u0103-\u0219i efectueze experimentele, s\u0103 salveze \u0219i s\u0103 \u00eemp\u0103rt\u0103\u0219easc\u0103 rezultatele muncii lor. \u00cen plus, utilizatorii avansa\u021bi pot accesa direct CLI-ul OpenShift din notebook-urile Jupyter pentru a utiliza primitivele Kubernetes, cum ar fi Job, sau func\u021bionalitatea OpenShift, de exemplu Tekton sau Knative. De asemenea, pot utiliza interfa\u021ba grafic\u0103 prietenoas\u0103 OpenShift, denumit\u0103 \"web console OpenShift\".<\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/601d63af1467b01df504954416effb3d.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/4647d8d979cc26b0c20cd3d63caed501.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p>Trec\u00e2nd la etapa urm\u0103toare, Open Data Hub ofer\u0103 posibilitatea de a gestiona conductele de date (data pipelines). Acest lucru se realizeaz\u0103 prin utilizarea unui obiect Ceph, care este oferit ca un depozit de date compatibil S3. Apache Spark asigur\u0103 streamingul datelor din surse externe sau din depozitul Ceph S3 \u00eencorporat, precum \u0219i permite efectuarea unor transform\u0103ri preliminare ale datelor. Apache Kafka ofer\u0103 gestionare avansat\u0103 a conductelor de date (unde se pot efectua \u00eenc\u0103rc\u0103ri multiple, precum \u0219i opera\u021biuni de transformare, analiz\u0103 \u0219i salvare a datelor).<\/p>\n<p>A\u0219adar, analistul de date a ob\u021binut acces la date \u0219i a construit un model. Acum el dore\u0219te s\u0103 \u00eemp\u0103rt\u0103\u0219easc\u0103 rezultatele ob\u021binute cu colegii sau dezvoltatorii de aplica\u021bii, oferindu-le modelul s\u0103u pe baza principiilor de tip serviciu. Pentru aceasta, este nevoie de un server de output, iar Open Data Hub are un astfel de server, care se nume\u0219te Seldon \u0219i permite publicarea modelului ca serviciu RESTful.<\/p>\n<p>\u00centr-un moment dat, pe serverul Seldon exist\u0103 mai multe astfel de modele \u0219i survine nevoia de a monitoriza cum sunt folosite. Pentru aceasta, Open Data Hub ofer\u0103 o colec\u021bie de metrici relevante \u0219i un motor de raportare bazat pe instrumente de monitorizare de uz larg, precum Prometheus \u0219i Grafana. Ca rezultat, ob\u021binem feedback pentru monitorizarea utiliz\u0103rii modelelor AI, \u00een special \u00een medii de produc\u021bie.<\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/1c95bcd2656ea8376ccc0a91fa266897.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p>Astfel, Open Data Hub asigur\u0103 o abordare cloud-like pe parcursul \u00eentregului ciclu de opera\u021biuni AI\/ML, \u00eencep\u00e2nd cu accesul \u0219i prepararea datelor \u0219i termin\u00e2nd cu antrenarea \u0219i exploatarea modelului \u00een industrie.<\/p>\n<h3>Adun\u0103m totul la un loc<\/h3>\n<p>\nAcum apare \u00eentrebarea cum s\u0103 organizeze toate acestea administratorul OpenShift. \u0218i aici intervine un operator special Kubernetes pentru proiectele Open Data Hub.<\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/4bcb406f6886227ef357cd0318767ca2.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p>Acest operator gestioneaz\u0103 instalarea, configurarea \u0219i ciclul de via\u021b\u0103 al proiectului Open Data Hub, inclusiv desf\u0103\u0219urarea instrumentelor men\u021bionate anterior, cum ar fi JupyterHub, Ceph, Spark, Kafka, Seldon, Prometheus \u0219i Grafana. Proiectul Open Data Hub poate fi g\u0103sit \u00een consola web OpenShift, la sec\u021biunea operatorilor din comunitate. Astfel, administratorul OpenShift poate specifica c\u0103 proiectele OpenShift corespunz\u0103toare apar\u021bin categoriei \u201eproiect Open Data Hub\u201d. Aceasta se face o singur\u0103 dat\u0103. Dup\u0103 aceea, analistul de date intr\u0103 \u00een spa\u021biul s\u0103u de proiect prin consola web OpenShift \u0219i vede c\u0103 operatorul Kubernetes corespunz\u0103tor este instalat \u0219i disponibil pentru proiectele sale. Apoi, acesta creeaz\u0103 un exemplu al proiectului Open Data Hub cu un singur clic \u0219i prime\u0219te imediat acces la instrumentele descrise mai sus. \u0218i toate acestea pot fi configurate \u00een moduri de disponibilitate \u00eenalt\u0103 \u0219i rezilien\u021b\u0103.<\/p>\n<p><img decoding=\"async\" alt=\"Proiectul Open Data Hub \u2013 o platform\u0103 deschis\u0103 de \u00eenv\u0103\u021bare automat\u0103 bazat\u0103 pe Red Hat OpenShift\" src=\"\/wp-content\/uploads\/2019\/10\/811a85229d0f3e54a96de1adb9aaeb24.jpg\" style=\"display:block;margin: 0 auto;\" \/><\/p>\n<p>Dac\u0103 dori\u021bi s\u0103 \u00eencerca\u021bi proiectul Open Data Hub, \u00eencepe\u021bi cu <noindex><a rel=\"nofollow\" href=\"https:\/\/opendatahub.io\/docs\/getting-started\/quick-installation.html\">instruc\u021biunile de instalare \u0219i un tutorial introductiv<\/a><\/noindex>. Detalii tehnice despre arhitectura Open Data Hub pot fi g\u0103site <noindex><a rel=\"nofollow\" href=\"https:\/\/opendatahub.io\/docs\/architecture.html\">aici<\/a><\/noindex>, planurile de dezvoltare ale proiectului \u2013 <noindex><a rel=\"nofollow\" href=\"https:\/\/opendatahub.io\/docs\/roadmap\/future.html\">aici<\/a><\/noindex>. \u00cen viitor, se preconizeaz\u0103 realizarea unei integr\u0103ri suplimentare cu Kubeflow, abordarea unor aspecte legate de reglementarea datelor \u0219i securitate, precum \u0219i organizarea integr\u0103rii cu sisteme bazate pe reguli Drools \u0219i Optaplanner. P\u0103rerea dumneavoastr\u0103 poate fi exprimat\u0103 \u0219i pute\u021bi deveni participant la proiectul <noindex><a rel=\"nofollow\" href=\"https:\/\/opendatahub.io\/docs\/additional.html#community\">Open Data Hub<\/a><\/noindex> pe pagina <noindex><a rel=\"nofollow\" href=\"https:\/\/opendatahub.io\/docs\/additional.html#community\">comunit\u0103\u021bii<\/a><\/noindex>. <\/p>\n<p>\u00cen concluzie: problemele grave de scalabilitate \u00eempiedic\u0103 organiza\u021biile s\u0103 valorifice pe deplin poten\u021bialul inteligen\u021bei artificiale \u0219i al \u00eenv\u0103\u021b\u0103rii automate. Red Hat OpenShift este folosit de mult timp cu succes pentru a rezolva probleme similare \u00een industria software-ului. Proiectul Open Data Hub, realizat \u00een cadrul comunit\u0103\u021bii de dezvoltare \u00een cod deschis, ofer\u0103 o arhitectur\u0103 de referin\u021b\u0103 pentru organizarea \u00eentregului ciclu opera\u021bional AI\/ML pe baza cloud-ului hibrid OpenShift. Avem un plan de dezvoltare clar \u0219i bine g\u00e2ndit pentru acest proiect, iar inten\u021bia noastr\u0103 este de a crea \u00een jurul s\u0103u o comunitate activ\u0103 \u0219i productiv\u0103 de dezvoltatori de solu\u021bii AI deschise pe platforma OpenShift.<br \/>\n<br \/>Sursa: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/redhatrussia\/blog\/472836\/\">habr.com<\/a><\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0411\u0443\u0434\u0443\u0449\u0435\u0435 \u043d\u0430\u0441\u0442\u0443\u043f\u0438\u043b\u043e, \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0443\u0436\u0435 \u0443\u0441\u043f\u0435\u0448\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u044e\u0442 \u0432\u0430\u0448\u0438 \u043b\u044e\u0431\u0438\u043c\u044b\u0435 \u043c\u0430\u0433\u0430\u0437\u0438\u043d\u044b, \u0442\u0440\u0430\u043d\u0441\u043f\u043e\u0440\u0442\u043d\u044b\u0435 \u043a\u043e\u043c\u043f\u0430\u043d\u0438\u0438 \u0438 \u0434\u0430\u0436\u0435 \u0444\u0435\u0440\u043c\u044b, \u0432\u044b\u0440\u0430\u0449\u0438\u0432\u0430\u044e\u0449\u0438\u0435 \u0438\u043d\u0434\u0435\u0435\u043a. \u0410 \u0435\u0441\u043b\u0438 \u0447\u0442\u043e-\u0442\u043e \u0441\u0443\u0449\u0435\u0441\u0442\u0432\u0443\u0435\u0442, \u0437\u043d\u0430\u0447\u0438\u0442 \u0432 \u0438\u043d\u0442\u0435\u0440\u043d\u0435\u0442\u0435 \u043f\u0440\u043e \u044d\u0442\u043e \u0443\u0436\u0435 \u0435\u0441\u0442\u044c\u2026 \u043e\u0442\u043a\u0440\u044b\u0442\u044b\u0439 \u043f\u0440\u043e\u0435\u043a\u0442! \u0421\u043c\u043e\u0442\u0440\u0438\u0442\u0435 \u043a\u0430\u043a Open Data Hub \u043f\u043e\u043c\u043e\u0433\u0430\u0435\u0442 \u043c\u0430\u0441\u0448\u0442\u0430\u0431\u0438\u0440\u043e\u0432\u0430\u0442\u044c \u043d\u043e\u0432\u044b\u0435 \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0438 \u0438\u0437\u0431\u0435\u0433\u0430\u0442\u044c \u0442\u0440\u0443\u0434\u043d\u043e\u0441\u0442\u0435\u0439 \u043f\u0440\u0438 \u0438\u0445 \u0432\u043d\u0435\u0434\u0440\u0435\u043d\u0438\u0438. \u041f\u0440\u0438 \u0432\u0441\u0435\u0445 \u043f\u043b\u044e\u0441\u0430\u0445 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 (artificial [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":39277,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-39276","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.2.1 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0411\u0443\u0434\u0443\u0449\u0435\u0435 \u043d\u0430\u0441\u0442\u0443\u043f\u0438\u043b\u043e, \u0442\u0435\u0445\u043d\u043e\u043b\u043e\u0433\u0438\u0438 \u0438\u0441\u043a\u0443\u0441\u0441\u0442\u0432\u0435\u043d\u043d\u043e\u0433\u043e \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0430 \u0438 \u043c\u0430\u0448\u0438\u043d\u043d\u043e\u0433\u043e \u043e\u0431\u0443\u0447\u0435\u043d\u0438\u044f \u0443\u0436\u0435 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