{"id":1084,"date":"2024-05-21T00:18:47","date_gmt":"2024-05-20T21:18:47","guid":{"rendered":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/?p=1084"},"modified":"2026-10-06T03:08:30","modified_gmt":"2026-10-06T00:08:30","slug":"yapay-zeka-ve-makine-ogrenimi-ile-bulut-bilisimin-sinir-tanimayan-gucu","status":"publish","type":"post","link":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/bilgi-islem\/yapay-zeka-ve-makine-ogrenimi-ile-bulut-bilisimin-sinir-tanimayan-gucu\/","title":{"rendered":"Yapay Zeka ve Makine \u00d6\u011frenimi ile Bulut Bili\u015fimin S\u0131n\u0131r Tan\u0131mayan G\u00fcc\u00fc"},"content":{"rendered":"
\n

Bulut altyap\u0131s\u0131 yapay zek\u00e2 ve makine \u00f6\u011frenmesi projelerinde veri haz\u0131rlama, model e\u011fitimi ve tahmin sunumu i\u00e7in kullan\u0131labilir. Bu a\u015famalar\u0131n kaynak ve i\u015fletim ihtiya\u00e7lar\u0131 farkl\u0131d\u0131r. Do\u011fru yakla\u015f\u0131m, \u00f6nce i\u015f hedefini ve de\u011ferlendirme \u00f6l\u00e7\u00fcs\u00fcn\u00fc belirlemek, sonra uygun hesaplama ve veri d\u00fczenini se\u00e7mektir; daha b\u00fcy\u00fck kaynak her zaman daha iyi model \u00fcretmez.<\/p>\n

\"Veri
Model paketi, veri ve de\u011ferlendirme kayd\u0131 birlikte izlenebilir olmal\u0131d\u0131r.
G\u00f6rseli b\u00fcy\u00fct \u2192<\/a><\/figcaption><\/figure>\n

\u0130\u015f hedefini model g\u00f6revine \u00e7evirin<\/h2>\n

Talep tahmini, belge s\u0131n\u0131fland\u0131rma ve kullan\u0131c\u0131ya yan\u0131t \u00fcretme ayn\u0131 ba\u015far\u0131 \u00f6l\u00e7\u00fcs\u00fcne sahip de\u011fildir. Hangi hatan\u0131n kabul edilebilir oldu\u011funu ve sonucun nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 belirleyin. Baz\u0131 s\u00fcre\u00e7lerde insan de\u011ferlendirmesi veya basit kurallar uygun ba\u015flang\u0131\u00e7 olabilir.<\/p>\n

Yaln\u0131z teknik do\u011fruluk \u00f6l\u00e7\u00fcs\u00fcn\u00fc de\u011fil gecikme, i\u015flem maliyeti ve yanl\u0131\u015f sonucun i\u015f etkisini de izleyin. Modelin yard\u0131mc\u0131 oldu\u011fu kararla do\u011frudan verdi\u011fi karar aras\u0131ndaki sorumluluk s\u0131n\u0131r\u0131n\u0131 a\u00e7\u0131k\u00e7a tan\u0131mlay\u0131n.<\/p>\n

Veri haz\u0131rl\u0131\u011f\u0131n\u0131 ayr\u0131 bir i\u015f y\u00fck\u00fc olarak planlay\u0131n<\/h2>\n

Verinin kayna\u011f\u0131, kullan\u0131m hakk\u0131, ki\u015fisel veri kapsam\u0131 ve g\u00fcncellenme s\u0131kl\u0131\u011f\u0131n\u0131 kaydedin. Veri d\u00f6n\u00fc\u015ft\u00fcrme, etiketleme ve kalite kontrolleri \u00f6nemli kaynak t\u00fcketebilir. Model e\u011fitimine ge\u00e7meden eksik veya hatal\u0131 verilerin nas\u0131l ele al\u0131naca\u011f\u0131n\u0131 belirleyin.<\/p>\n

E\u011fitim, do\u011frulama ve test ayr\u0131m\u0131n\u0131 ger\u00e7ek kullan\u0131m ko\u015fullar\u0131na g\u00f6re yap\u0131n. Ayn\u0131 kullan\u0131c\u0131 veya birbirine \u00e7ok benzeyen kay\u0131tlar farkl\u0131 k\u00fcmelere geli\u015fig\u00fczel da\u011f\u0131l\u0131rsa sonu\u00e7 oldu\u011fundan iyi g\u00f6r\u00fcnebilir. Veri s\u00fcr\u00fcm\u00fc olmadan deneyleri kar\u015f\u0131la\u015ft\u0131rmak zorla\u015f\u0131r.<\/p>\n

E\u011fitim ve \u00e7\u0131kar\u0131m kapasitesini ay\u0131r\u0131n<\/h2>\n\n\n\n\n\n\n\n
A\u015fama<\/th>\n\u00d6ncelikli \u00f6l\u00e7\u00fcler<\/th>\n<\/tr>\n<\/thead>\n
Veri haz\u0131rlama<\/td>\nDisk\/a\u011f ak\u0131\u015f\u0131, bellek ve d\u00f6n\u00fc\u015f\u00fcm s\u00fcresi.<\/td>\n<\/tr>\n
E\u011fitim<\/td>\nModel\/veri boyutu, CPU\/GPU kullan\u0131m\u0131, bellek ve deney s\u00fcresi.<\/td>\n<\/tr>\n
\u00c7\u0131kar\u0131m<\/td>\n\u0130stek gecikmesi, e\u015fzamanl\u0131 kullan\u0131m, hata oran\u0131 ve birim maliyet.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

GPU, uygun model ve yaz\u0131l\u0131mda h\u0131zland\u0131rma sa\u011flayabilir; her g\u00f6revde zorunlu veya daha ekonomik de\u011fildir. Modelin belle\u011fe s\u0131\u011fmas\u0131, veri aktar\u0131m\u0131 ve s\u00fcr\u00fcc\u00fc uyumu birlikte kontrol edilir. E\u011fitimde iyi \u00e7al\u0131\u015fan yap\u0131land\u0131rmay\u0131 \u00fcretim \u00e7\u0131kar\u0131m\u0131na do\u011frudan kopyalamay\u0131n.<\/p>\n

Deneyi ve model paketini s\u00fcr\u00fcmleyin<\/h2>\n

Kaynak kod, veri, parametre, \u00e7al\u0131\u015fma ortam\u0131 ve sonu\u00e7 ili\u015fkisini kaydedin. MLflow gibi ara\u00e7lar deney izlemeye yard\u0131mc\u0131 olabilir; ara\u00e7 kurulumu ve y\u00f6netimi ayr\u0131ca planlan\u0131r. Model dosyas\u0131n\u0131n yan\u0131nda beklenen girdi \u015femas\u0131n\u0131 ve \u00f6n i\u015fleme ad\u0131mlar\u0131n\u0131 da saklay\u0131n.<\/p>\n

Yay\u0131n \u00f6ncesinde temsili ve hatal\u0131 girdileri de\u011ferlendirin. Eri\u015fim kimliklerini model veya kaynak kod dosyas\u0131na g\u00f6mmeyin. Hassas verinin loglara veya \u00fc\u00e7\u00fcnc\u00fc taraf model hizmetlerine hangi ko\u015fulda aktar\u0131labilece\u011fini belirleyin.<\/p>\n

\u00dcretimde kalite ve geri d\u00f6n\u00fc\u015f yolu<\/h2>\n

Veri da\u011f\u0131l\u0131m\u0131 de\u011fi\u015febilir; eski test sonucu yeni kullan\u0131m\u0131 tam temsil etmeyebilir. Girdi kalitesi, yan\u0131t davran\u0131\u015f\u0131 ve uygun \u00e7\u0131kt\u0131 de\u011ferlendirmelerini izleyin. Otomatik g\u00fcncelleme veya yeniden e\u011fitim \u00f6ncesinde kabul ko\u015fullar\u0131n\u0131 tan\u0131mlay\u0131n.<\/p>\n

FibaCloud \u00fczerinde \u00e7al\u0131\u015facak uygulaman\u0131z i\u00e7in mevcut hesaplama, depolama ve a\u011f se\u00e7eneklerini gereksinimle e\u015fle\u015ftirin. Belirli bir haz\u0131r AI platformu, GPU veya model hizmetini kapsam do\u011frulamas\u0131 olmadan varsaymay\u0131n. \u00d6nceki model s\u00fcr\u00fcm\u00fcne ge\u00e7i\u015fi veri ve aray\u00fcz uyumuyla birlikte test edin.<\/p>\n

S\u0131k sorulan sorular<\/h2>\n
\n
E\u011fitim ile \u00e7\u0131kar\u0131m ayn\u0131 altyap\u0131y\u0131 gerektirir mi?<\/strong><\/dt>\n
\n

Her zaman de\u011fil. E\u011fitim toplu ve yo\u011fun hesaplama isterken \u00e7\u0131kar\u0131m gecikme ve e\u015fzamanl\u0131 istek hedefleriyle y\u00f6netilebilir.<\/p>\n<\/dd>\n

Modeli yeniden e\u011fitmek her kalite sorununu \u00e7\u00f6zer mi?<\/strong><\/dt>\n
\n

Hay\u0131r. Girdi hatas\u0131, hedef tan\u0131m\u0131, veri da\u011f\u0131l\u0131m\u0131 ve entegrasyon da incelenmelidir. Yeni model ayr\u0131ca de\u011ferlendirilmelidir.<\/p>\n<\/dd>\n<\/dl>\n

Sonraki ad\u0131m<\/h2>\n

\u00d6\u011frenme temelini yapay zek\u00e2 e\u011fitimi<\/a> ve uygulama teslimini kontroll\u00fc yay\u0131n s\u00fcreci<\/a> ile tamamlay\u0131n.<\/p>\n

Kaynaklar<\/h2>\n
    \n
  1. NIST AI 100-1 \u2014 AI Risk Management Framework 1.0<\/a><\/li>\n
  2. MLflow \u2014 Experiment Tracking<\/a><\/li>\n
  3. PyTorch \u2014 Tutorials<\/a><\/li>\n<\/ol>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Bulut altyap\u0131s\u0131 yapay zek\u00e2 ve makine \u00f6\u011frenmesi projelerinde veri haz\u0131rlama, model e\u011fitimi ve tahmin sunumu i\u00e7in kullan\u0131labilir. Bu a\u015famalar\u0131n kaynak ve i\u015fletim ihtiya\u00e7lar\u0131 farkl\u0131d\u0131r. Do\u011fru yakla\u015f\u0131m, \u00f6nce i\u015f hedefini ve de\u011ferlendirme \u00f6l\u00e7\u00fcs\u00fcn\u00fc belirlemek, sonra uygun hesaplama ve veri d\u00fczenini se\u00e7mektir; daha b\u00fcy\u00fck kaynak her zaman daha iyi model \u00fcretmez. Model paketi, veri ve de\u011ferlendirme kayd\u0131 […]<\/p>\n","protected":false},"author":1,"featured_media":1387,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26,24,25,1],"tags":[74,202,180],"class_list":["post-1084","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bilgi-islem","category-bulut-danismanlik-hizmeti","category-buluta-genel-bakis","category-genel","tag-bulut-bilisim","tag-makine-ogrenimi","tag-yapay-zeka"],"_links":{"self":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts\/1084","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/comments?post=1084"}],"version-history":[{"count":5,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts\/1084\/revisions"}],"predecessor-version":[{"id":1091,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/posts\/1084\/revisions\/1091"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/media\/1387"}],"wp:attachment":[{"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/media?parent=1084"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/categories?post=1084"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.fibacloud.com\/tr\/kaynaklar\/wp-json\/wp\/v2\/tags?post=1084"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}