{"id":29805,"date":"2025-09-19T23:30:23","date_gmt":"2025-09-19T20:30:23","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/amazon-sagemaker-hazir-modeller-ve-makine-ogrenmesi-yasam-dongusu-mini-proje\/"},"modified":"2025-09-19T23:30:23","modified_gmt":"2025-09-19T20:30:23","slug":"amazon-sagemaker-hazir-modeller-ve-makine-ogrenmesi-yasam-dongusu-mini-proje","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/amazon-sagemaker-hazir-modeller-ve-makine-ogrenmesi-yasam-dongusu-mini-proje\/","title":{"rendered":"Amazon SageMaker Haz\u0131r Modeller ve Makine \u00d6\u011frenmesi Ya\u015fam D\u00f6ng\u00fcs\u00fc: Mini Proje"},"content":{"rendered":"

Amazon SageMaker Haz\u0131r Modeller ve Makine \u00d6\u011frenmesi Ya\u015fam D\u00f6ng\u00fcs\u00fc: Mini Proje<\/p>\n

Amazon SageMaker, makine \u00f6\u011frenmesi (ML) modelleri olu\u015fturma, e\u011fitme ve da\u011f\u0131t\u0131m\u0131 i\u00e7in kapsaml\u0131 bir bulut hizmetidir. Bu makalede, SageMaker'\u0131n haz\u0131r modellerini kullanarak bir mini proje geli\u015ftirmeyi ve makine \u00f6\u011frenmesi ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn pratik uygulamalar\u0131n\u0131 inceleyece\u011fiz. Haz\u0131r modellerin avantajlar\u0131n\u0131, ya\u015fam d\u00f6ng\u00fcs\u00fc a\u015famalar\u0131n\u0131 ve ger\u00e7ek d\u00fcnya senaryolar\u0131n\u0131 ele alarak, hem yeni ba\u015flayanlar hem de deneyimli geli\u015ftiriciler i\u00e7in kapsaml\u0131 bir rehber sunmay\u0131 ama\u00e7l\u0131yoruz.<\/p>\n

Amazon SageMaker Haz\u0131r Modelleri Nedir?<\/h2>\n

Amazon SageMaker, \u00f6nceden e\u011fitilmi\u015f ve \u00e7e\u015fitli g\u00f6revler i\u00e7in optimize edilmi\u015f \u00e7ok say\u0131da haz\u0131r model sunar. Bu modeller, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma, do\u011fal dil i\u015fleme, tahmine dayal\u0131 analiz gibi farkl\u0131 alanlarda kullan\u0131labilir. Haz\u0131r modeller, kendi modelinizi e\u011fitme zahmetinden ka\u00e7\u0131nman\u0131za ve h\u0131zl\u0131ca bir prototip olu\u015fturman\u0131za olanak tan\u0131r. Bununla birlikte, haz\u0131r modellerin s\u0131n\u0131rlamalar\u0131n\u0131 da anlamak \u00f6nemlidir. \u00d6rne\u011fin, \u00f6zel bir veri k\u00fcmenize tam olarak uymayabilirler veya belirli bir g\u00f6reve \u00f6zel olarak optimize edilmemi\u015f olabilirler. Ancak, bir projenin ilk a\u015famalar\u0131nda veya h\u0131zl\u0131 bir \u00e7\u00f6z\u00fcm gerekti\u011finde olduk\u00e7a de\u011ferlidirler. \u00d6zellikle, b\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken veya geli\u015fmi\u015f uzmanl\u0131\u011fa sahip bir ekip yoksa, zaman ve maliyetten tasarruf sa\u011flarlar. \u00d6rne\u011fin, bir g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma projesinde \u00f6nceden e\u011fitilmi\u015f bir ResNet modeli kullanarak, s\u0131f\u0131rdan model e\u011fitimine k\u0131yasla \u00e7ok daha h\u0131zl\u0131 sonu\u00e7lar elde edebilirsiniz. Daha fazla bilgi i\u00e7in fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz.<\/p>\n

Makine \u00d6\u011frenmesi Ya\u015fam D\u00f6ng\u00fcs\u00fc: A\u015famalar\u0131 Nas\u0131l Anla\u015f\u0131l\u0131r?<\/h2>\n

Bir makine \u00f6\u011frenmesi projesinin ba\u015far\u0131s\u0131, iyi tan\u0131mlanm\u0131\u015f bir ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc takip etmeye ba\u011fl\u0131d\u0131r. Bu d\u00f6ng\u00fc, veri toplama, veri \u00f6n i\u015fleme, model e\u011fitimi, model de\u011ferlendirmesi, model da\u011f\u0131t\u0131m\u0131 ve model izleme a\u015famalar\u0131n\u0131 i\u00e7erir. Her a\u015fama, projenin genel ba\u015far\u0131s\u0131 i\u00e7in kritik \u00f6neme sahiptir. \u00d6rne\u011fin, yetersiz veri \u00f6n i\u015fleme, modelin performans\u0131n\u0131 ciddi \u015fekilde etkileyebilir. Benzer \u015fekilde, modelin performans\u0131n\u0131 d\u00fczenli olarak izlemek, performans d\u00fc\u015f\u00fc\u015flerini erken tespit etmek ve gerekli ayarlamalar\u0131 yapmak i\u00e7in \u00f6nemlidir. Bu ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc anlamak, gelecekteki projelerde daha verimli ve etkili \u00e7al\u0131\u015fman\u0131za olanak tan\u0131r. Her ad\u0131m\u0131n detaylar\u0131na daha yak\u0131ndan bakal\u0131m.<\/p>\n

Veri Toplama ve \u00d6n \u0130\u015fleme: Nas\u0131l Yap\u0131l\u0131r?<\/h3>\n

Her makine \u00f6\u011frenmesi projesi, kaliteli verilerle ba\u015flar. Veri toplama, projenin amac\u0131na ve t\u00fcr\u00fcne ba\u011fl\u0131 olarak \u00e7e\u015fitli kaynaklardan (\u00f6rne\u011fin, veritabanlar\u0131, API'ler, sens\u00f6rler) ger\u00e7ekle\u015ftirilebilir. Toplanan verilerin temizlenmesi, d\u00f6n\u00fc\u015ft\u00fcr\u00fclmesi ve modele uygun hale getirilmesi i\u00e7in veri \u00f6n i\u015fleme ad\u0131mlar\u0131 gereklidir. Bu ad\u0131mlar, eksik de\u011ferlerin ele al\u0131nmas\u0131, g\u00fcr\u00fclt\u00fcl\u00fc verilerin filtrelenmesi ve verilerin \u00f6l\u00e7eklendirilmesini i\u00e7erir. Veri \u00f6n i\u015fleme, modelin do\u011frulu\u011funu ve performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde etkiler. \u00d6rne\u011fin, eksik de\u011ferlerin yanl\u0131\u015f i\u015flenmesi, modelin \u00f6nyarg\u0131l\u0131 sonu\u00e7lar \u00fcretmesine yol a\u00e7abilir. Bu nedenle, veri \u00f6n i\u015fleme a\u015famas\u0131na b\u00fcy\u00fck \u00f6nem vermek gerekmektedir.<\/p>\n

Model E\u011fitimi: Ad\u0131m Ad\u0131m Rehber<\/h3>\n

Veri \u00f6n i\u015fleme tamamland\u0131ktan sonra, model e\u011fitimine ge\u00e7ebiliriz. SageMaker, \u00e7e\u015fitli algoritmalar ve \u00e7er\u00e7eveler kullanarak model e\u011fitimini kolayla\u015ft\u0131r\u0131r. Haz\u0131r modeller kullan\u0131yorsan\u0131z, bu ad\u0131m genellikle daha az karma\u015f\u0131kt\u0131r, \u00e7\u00fcnk\u00fc modelin e\u011fitimi \u00f6nceden yap\u0131lm\u0131\u015ft\u0131r. Ancak, hiperparametre ayarlamas\u0131 gibi ince ayarlar yapman\u0131z gerekebilir. Hiperparametre ayarlamas\u0131, modelin performans\u0131n\u0131 optimize etmek i\u00e7in kullan\u0131lan bir tekniktir. Farkl\u0131 hiperparametre de\u011ferleri deneyerek, modelin performans\u0131n\u0131 en \u00fcst d\u00fczeye \u00e7\u0131karabilirsiniz. Bu ad\u0131m, modelin do\u011frulu\u011fu ve genel performans\u0131 \u00fczerinde \u00f6nemli bir etkiye sahiptir. \u00d6rne\u011fin, yanl\u0131\u015f hiperparametre ayarlar\u0131, modelin a\u015f\u0131r\u0131 \u00f6\u011frenme veya yeter \u00f6\u011frenme sorunlar\u0131yla kar\u015f\u0131la\u015fmas\u0131na neden olabilir.<\/p>\n

Model De\u011ferlendirmesi: Performans\u0131 Nas\u0131l \u00d6l\u00e7eriz?<\/h3>\n

E\u011fitim tamamland\u0131ktan sonra, modelin performans\u0131n\u0131 de\u011ferlendirmek \u00e7ok \u00f6nemlidir. Bu, e\u011fitim verileriyle g\u00f6r\u00fclmeyen bir test veri k\u00fcmesi kullan\u0131larak yap\u0131l\u0131r. Performans \u00f6l\u00e7\u00fctleri, projenin amac\u0131na ba\u011fl\u0131 olarak de\u011fi\u015fir; ancak yayg\u0131n \u00f6l\u00e7\u00fctler aras\u0131nda do\u011fruluk, hassasiyet, geri \u00e7a\u011f\u0131rma ve F1 puan\u0131 bulunur. Modelin performans\u0131 beklentileri kar\u015f\u0131lam\u0131yorsa, e\u011fitim s\u00fcrecine geri d\u00f6n\u00fcp hiperparametreleri ayarlamak, farkl\u0131 bir model se\u00e7mek veya veri \u00f6n i\u015fleme ad\u0131mlar\u0131n\u0131 yeniden g\u00f6zden ge\u00e7irmek gerekebilir. Bu a\u015famada, modelin performans\u0131n\u0131 etkileyebilecek potansiyel sorunlar\u0131 belirlemek ve bunlar\u0131 \u00e7\u00f6zmek i\u00e7in derinlemesine bir analiz yap\u0131lmas\u0131 gerekir.<\/p>\n

Ger\u00e7ek D\u00fcnya Senaryolar\u0131 ve Vaka \u00c7al\u0131\u015fmalar\u0131<\/h2>\n

Amazon SageMaker'\u0131n haz\u0131r modellerinin ve makine \u00f6\u011frenmesi ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn ger\u00e7ek d\u00fcnya uygulamalar\u0131n\u0131 g\u00f6relim. Birinci vaka \u00e7al\u0131\u015fmas\u0131, bir e-ticaret \u015firketinin m\u00fc\u015fteri davran\u0131\u015flar\u0131n\u0131 tahmin etmek i\u00e7in kullan\u0131labilecek bir modeldir. \u015eirket, m\u00fc\u015fterilerin ge\u00e7mi\u015f sat\u0131n alma ge\u00e7mi\u015fi, tarama ge\u00e7mi\u015fi ve demografik bilgilerini kullanarak, gelecekteki sat\u0131n al\u0131mlar\u0131 tahmin etmek i\u00e7in bir tavsiye sistemi olu\u015fturabilir. Bu, m\u00fc\u015fteri deneyimini iyile\u015ftirmek ve sat\u0131\u015flar\u0131 art\u0131rmak i\u00e7in kullan\u0131labilir. \u0130kinci vaka \u00e7al\u0131\u015fmas\u0131 ise bir sa\u011fl\u0131k kurulu\u015funun hastal\u0131k te\u015fhisini iyile\u015ftirmek i\u00e7in kullan\u0131labilecek bir modeldir. Kurulu\u015f, t\u0131bbi g\u00f6r\u00fcnt\u00fcleme verilerini kullanarak, hastal\u0131klar\u0131 daha do\u011fru ve h\u0131zl\u0131 bir \u015fekilde te\u015fhis etmek i\u00e7in bir makine \u00f6\u011frenmesi modeli e\u011fitebilir. Bu, hastalar i\u00e7in daha iyi sonu\u00e7lar sa\u011flayabilir. Her iki vaka \u00e7al\u0131\u015fmas\u0131 da haz\u0131r modellerin ve ML ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn g\u00fcc\u00fcn\u00fc g\u00f6stermektedir.<\/p>\n

\u0130leri D\u00fczey Teknikler ve \u0130pu\u00e7lar\u0131<\/h2>\n

Deneyimli kullan\u0131c\u0131lar i\u00e7in, model optimizasyonu, da\u011f\u0131t\u0131m stratejileri ve model izleme gibi ileri d\u00fczey konular\u0131 ele alal\u0131m. Model optimizasyonu, modelin performans\u0131n\u0131 daha da geli\u015ftirmek i\u00e7in kullan\u0131l\u0131r. Bu, hiperparametre ayarlamas\u0131, \u00f6zellik m\u00fchendisli\u011fi ve model se\u00e7imi gibi teknikleri i\u00e7erir. Da\u011f\u0131t\u0131m stratejileri, modelin \u00fcretme ortam\u0131na nas\u0131l da\u011f\u0131t\u0131laca\u011f\u0131n\u0131 belirler. Bu, modelin \u00f6l\u00e7eklenebilirli\u011fini ve performans\u0131n\u0131 etkiler. Model izleme, modelin performans\u0131n\u0131 d\u00fczenli olarak izlemeyi ve olas\u0131 sorunlar\u0131 tespit etmeyi i\u00e7erir. Bu, modelin s\u00fcrekli olarak do\u011fru ve g\u00fcvenilir sonu\u00e7lar \u00fcretmesini sa\u011flamak i\u00e7in \u00f6nemlidir. Bu ileri d\u00fczey konular, ML projelerinin ba\u015far\u0131s\u0131 i\u00e7in kritik \u00f6neme sahiptir.<\/p>\n

\u00d6\u011frenme Yol Haritas\u0131<\/h2>\n

Makine \u00f6\u011frenmesi ve Amazon SageMaker'\u0131 \u00f6\u011frenmek i\u00e7in bir yol haritas\u0131 sunuyoruz:<\/p>\n\n\n\n\n\n
Seviye<\/th>\n\u00d6neriler<\/th>\n<\/tr>\n
Yeni Ba\u015flayan<\/td>\nAmazon SageMaker'\u0131n temel kavramlar\u0131n\u0131 \u00f6\u011frenin. Haz\u0131r modellerle basit projeler ger\u00e7ekle\u015ftirin. fatihsoysal.com<\/a> sitesinde bulunan kaynaklardan yararlan\u0131n.<\/td>\n<\/tr>\n
Orta<\/td>\nDaha karma\u015f\u0131k projeler ger\u00e7ekle\u015ftirin. Kendi veri k\u00fcmenizi kullanarak model e\u011fitimi yap\u0131n. Hiperparametre ayarlama ve model de\u011ferlendirme tekniklerini uygulay\u0131n.<\/td>\n<\/tr>\n
\u0130leri D\u00fczey<\/td>\nModel optimizasyonu, da\u011f\u0131t\u0131m stratejileri ve model izleme konular\u0131nda uzmanla\u015f\u0131n. B\u00fcy\u00fck \u00f6l\u00e7ekli projelerde \u00e7al\u0131\u015f\u0131n. Kendi \u00f6zel modellerinizi geli\u015ftirin.<\/td>\n<\/tr>\n<\/table>\n

Sonu\u00e7<\/h2>\n

Amazon SageMaker'\u0131n haz\u0131r modelleri, makine \u00f6\u011frenmesi projelerini h\u0131zland\u0131rmak ve basitle\u015ftirmek i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Bu makalede, SageMaker'\u0131n haz\u0131r modellerini kullanarak bir mini proje geli\u015ftirmeyi ve makine \u00f6\u011frenmesi ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn pratik uygulamalar\u0131n\u0131 inceledik. Umar\u0131m bu makale, hem yeni ba\u015flayanlar hem de deneyimli geli\u015ftiriciler i\u00e7in de\u011ferli bilgiler sunmu\u015ftur. Daha fazla bilgi i\u00e7in fatihsoysal.com<\/a> sitesini ziyaret edebilirsiniz.<\/p>\n

S\u0131k\u00e7a Sorulan Sorular<\/h3>\n
    \n
  • Haz\u0131r modellerin s\u0131n\u0131rlamalar\u0131 nelerdir?<\/strong> Haz\u0131r modeller, \u00f6zel veri k\u00fcmelerine tam olarak uymayabilir veya belirli bir g\u00f6reve \u00f6zel olarak optimize edilmemi\u015f olabilirler.<\/li>\n
  • Hiperparametre ayarlama nedir?<\/strong> Modelin performans\u0131n\u0131 optimize etmek i\u00e7in kullan\u0131lan bir tekniktir.<\/li>\n
  • Model de\u011ferlendirmesi neden \u00f6nemlidir?<\/strong> Modelin performans\u0131n\u0131 \u00f6l\u00e7mek ve olas\u0131 sorunlar\u0131 tespit etmek i\u00e7in \u00f6nemlidir.<\/li>\n
  • Model izleme nas\u0131l yap\u0131l\u0131r?<\/strong> Modelin performans\u0131n\u0131 d\u00fczenli olarak izleyerek ve olas\u0131 sorunlar\u0131 tespit ederek yap\u0131l\u0131r.<\/li>\n
  • SageMaker'da hangi programlama dillerini kullanabilirim?<\/strong> Python, R ve di\u011fer bir\u00e7ok dil SageMaker ile uyumludur.<\/li>\n<\/ul>\n

    Yazar: Fatih Soysal<\/p>\n<\/pre>\n

    <\/code><\/p>\n","protected":false},"excerpt":{"rendered":"Amazon SageMaker Haz\u0131r Modeller ve Makine \u00d6\u011frenmesi Ya\u015fam D\u00f6ng\u00fcs\u00fc: Mini Proje Amazon SageMaker, makine \u00f6\u011frenmesi (ML) modelleri olu\u015fturma,…","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_page_header_type":"","csco_page_load_nextpost":"","csco_page_subscribe_form":"","csco_page_contact_form":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-29805","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nAmazon SageMaker Haz\u0131r Modeller ve Makine \u00d6\u011frenmesi Ya\u015fam D\u00f6ng\u00fcs\u00fc: Mini Proje<\/title>\n<meta name=\"description\" content=\"Amazon SageMaker, makine \u00f6\u011frenmesi (ML) modelleri olu\u015fturma, e\u011fitme ve da\u011f\u0131t\u0131m\u0131 i\u00e7in kapsaml\u0131 bir bulut hizmetidir. Bu makalede, SageMaker'\u0131n haz\u0131r modellerini kullanarak bir mini proje geli\u015ftirmeyi ve makine \u00f6\u011frenmesi ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn pratik uygulamalar\u0131n\u0131 inceleyece\u011fiz. 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