Makine \u00f6\u011frenimi projelerinin ba\u015far\u0131ya ula\u015fmas\u0131nda sadece iyi bir model geli\u015ftirmek yeterli de\u011fildir; ayn\u0131 zamanda bu modellerin ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fc etkin bir \u015fekilde y\u00f6netmek b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r. MLOps (Makine \u00d6\u011frenimi Operasyonlar\u0131), model geli\u015ftirme, da\u011f\u0131t\u0131m ve izleme s\u00fcre\u00e7lerini otomatize etmeyi ve standartla\u015ft\u0131rmay\u0131 ama\u00e7layan bir k\u00fclt\u00fcr ve pratikler b\u00fct\u00fcn\u00fcd\u00fcr. Bu makalede, \u00f6zellikle yerel odakl\u0131 (local-first) bir yakla\u015f\u0131mla MLOps s\u00fcre\u00e7lerini nas\u0131l optimize edebilece\u011fimizi ve bu s\u00fcre\u00e7te Skyulf adl\u0131 platformun sundu\u011fu \u00e7\u00f6z\u00fcmleri \u00f6zellik m\u00fchendisli\u011finden model da\u011f\u0131t\u0131m\u0131na kadar ad\u0131m ad\u0131m inceleyece\u011fiz.<\/p>\n
Makine \u00f6\u011frenimi projeleri, veri haz\u0131rl\u0131\u011f\u0131ndan model da\u011f\u0131t\u0131m\u0131na kadar karma\u015f\u0131k ve \u00e7ok a\u015famal\u0131 s\u00fcre\u00e7ler i\u00e7erir. Bu s\u00fcre\u00e7lerin manuel y\u00f6netimi, tutars\u0131zl\u0131klara, hatalara ve yava\u015flamalara yol a\u00e7abilir. MLOps, bu zorluklar\u0131n \u00fcstesinden gelmek i\u00e7in geli\u015ftirilmi\u015ftir.<\/p>\n
Geleneksel MLOps yakla\u015f\u0131mlar\u0131 genellikle bulut tabanl\u0131 hizmetlere odaklan\u0131r. Bu durum, \u00f6zellikle ba\u015flang\u0131\u00e7 a\u015famas\u0131ndaki veya hassas veri i\u00e7eren projelerde baz\u0131 zorluklar\u0131 beraberinde getirebilir:<\/p>\n
Yerel odakl\u0131 bir MLOps yakla\u015f\u0131m\u0131, geli\u015ftirme ve ilk test a\u015famalar\u0131n\u0131n b\u00fcy\u00fck \u00f6l\u00e7\u00fcde yerel makinelerde veya \u015firket i\u00e7i sunucularda yap\u0131lmas\u0131n\u0131 te\u015fvik eder. Bu yakla\u015f\u0131m, yukar\u0131da bahsedilen zorluklara \u00e7\u00f6z\u00fcm sunar:<\/p>\n
Skyulf, yerel odakl\u0131 MLOps i\u015f ak\u0131\u015flar\u0131n\u0131 kolayla\u015ft\u0131rmak i\u00e7in tasarlanm\u0131\u015f, u\u00e7tan uca bir platformdur. \u00d6zellik m\u00fchendisli\u011finden model da\u011f\u0131t\u0131m\u0131na kadar t\u00fcm s\u00fcre\u00e7leri yerel ortamda y\u00f6netme yetene\u011fi sunarak, geli\u015ftiricilere bulut ba\u011f\u0131ml\u0131l\u0131\u011f\u0131n\u0131 azaltma ve projelerini daha h\u0131zl\u0131 ve g\u00fcvenli bir \u015fekilde ilerletme imkan\u0131 tan\u0131r.<\/p>\n
Skyulf, makine \u00f6\u011frenimi ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn her a\u015famas\u0131nda geli\u015ftiricilere destek olan, yerel odakl\u0131 bir MLOps platformudur. Mod\u00fcler yap\u0131s\u0131 sayesinde, mevcut ara\u00e7larla kolayca entegre olabilirken, kendi b\u00fcnyesinde de g\u00fc\u00e7l\u00fc \u00f6zellikler sunar.<\/p>\n
Skyulf, bir dizi entegre bile\u015fenle gelir ve bu bile\u015fenler, MLOps i\u015f ak\u0131\u015f\u0131n\u0131n farkl\u0131 a\u015famalar\u0131n\u0131 destekler:<\/p>\n
Skyulf’un en belirgin \u00f6zelli\u011fi, yerel geli\u015ftirme deneyimini optimize etmesidir:<\/p>\n
Skyulf, a\u00e7\u0131k standartlar\u0131 ve pop\u00fcler makine \u00f6\u011frenimi k\u00fct\u00fcphanelerini destekler. Scikit-learn, TensorFlow, PyTorch gibi k\u00fct\u00fcphanelerle sorunsuz \u00e7al\u0131\u015f\u0131rken, mevcut CI\/CD boru hatlar\u0131n\u0131za da entegre edilebilir. Bu esneklik, ekiplerin mevcut ara\u00e7lar\u0131n\u0131 terk etmeden Skyulf’un avantajlar\u0131ndan yararlanmas\u0131n\u0131 sa\u011flar.<\/p>\n
\u00d6zellik m\u00fchendisli\u011fi, ham veriyi makine \u00f6\u011frenimi modellerinin daha iyi \u00f6\u011frenebilece\u011fi formatlara d\u00f6n\u00fc\u015ft\u00fcrme sanat\u0131d\u0131r. Skyulf, bu kritik s\u00fcreci daha d\u00fczenli, izlenebilir ve yeniden \u00fcretilebilir hale getirir.<\/p>\n
Her \u00f6zellik m\u00fchendisli\u011fi s\u00fcreci, veri ke\u015ffi ve temizli\u011fi ile ba\u015flar. Skyulf, veri setlerinin versiyonlanmas\u0131 ve meta verilerinin kaydedilmesiyle bu a\u015famay\u0131 destekler.<\/p>\n
\nimport skyulf\n\n# Veri setini y\u00fckle ve versiyonla\ndata = skyulf.load_data(\"raw_customer_data.csv\")\nskyulf.version_data(data, name=\"musteri_ham_veri_v1\", description=\"\u0130lk ham m\u00fc\u015fteri verisi\")\n\n# Veri temizleme ve \u00f6n i\u015fleme ad\u0131mlar\u0131\ncleaned_data = data.dropna()\ncleaned_data = skyulf.process_outliers(cleaned_data)\n\nskyulf.version_data(cleaned_data, name=\"musteri_temiz_veri_v1\", parent=\"musteri_ham_veri_v1\")\n<\/pre>\n<\/code><\/p>\n
\u00d6zellik Olu\u015fturma ve D\u00f6n\u00fc\u015ft\u00fcrme<\/h4>\n
Skyulf'un yerel \u00f6zellik deposu, olu\u015fturulan \u00f6zellikleri merkezi bir yerde saklamay\u0131 ve y\u00f6netmeyi sa\u011flar. Bu, farkl\u0131 modellerin ayn\u0131 tutarl\u0131 \u00f6zellikleri kullanmas\u0131na olanak tan\u0131r.<\/p>\n
\n# \u00d6zellik olu\u015fturma fonksiyonlar\u0131\ndef create_age_bins(df):\n df['age_bin'] = pd.cut(df['age'], bins=[0, 18, 35, 60, 100], labels=['Gen\u00e7', 'Yeti\u015fkin', 'Orta Ya\u015f', 'Ya\u015fl\u0131'])\n return df\n\ndef encode_categorical(df):\n df = pd.get_dummies(df, columns=['gender', 'city'])\n return df\n\n# \u00d6zellikleri uygula\nfeatures = create_age_bins(cleaned_data)\nfeatures = encode_categorical(features)\n\n# \u00d6zellikleri Skyulf Feature Store'a kaydet\nskyulf.feature_store.save_features(\n features,\n name=\"musteri_segmentasyon_ozellikleri_v1\",\n description=\"M\u00fc\u015fteri segmentasyonu i\u00e7in ya\u015f ve demografik \u00f6zellikler\",\n tags=[\"segmentation\", \"demographic\"]\n)\n<\/pre>\n<\/code><\/p>\n
\u00d6zellik Depolama (Feature Store) ve Versiyonlama<\/h4>\n
Skyulf'un yerel \u00f6zellik deposu, \u00f6zelliklerin versiyonlanmas\u0131n\u0131 ve meta verilerinin kaydedilmesini sa\u011flar. Bu sayede, hangi modelin hangi \u00f6zellik versiyonunu kulland\u0131\u011f\u0131 net bir \u015fekilde izlenebilir.<\/p>\n
\n\n\n\u00d6zellik Ad\u0131<\/th>\n Versiyon<\/th>\n A\u00e7\u0131klama<\/th>\n Olu\u015fturulma Tarihi<\/th>\n<\/tr>\n<\/thead>\n \n\nage_bin<\/td>\n v1.0<\/td>\n Ya\u015f gruplar\u0131na ayr\u0131lm\u0131\u015f kategori<\/td>\n 2023-10-26<\/td>\n<\/tr>\n \ngender_Male<\/td>\n v1.0<\/td>\n Cinsiyet 'Erkek' dummy de\u011fi\u015fkeni<\/td>\n 2023-10-26<\/td>\n<\/tr>\n \ncity_Istanbul<\/td>\n v1.0<\/td>\n \u015eehir '\u0130stanbul' dummy de\u011fi\u015fkeni<\/td>\n 2023-10-26<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\nModel Geli\u015ftirme ve Deney Takibi<\/h3>\n
\u00d6zellikler haz\u0131rland\u0131ktan sonra s\u0131ra model geli\u015ftirmeye gelir. Skyulf'un deney takip sistemi, model e\u011fitim s\u00fcre\u00e7lerini izlemeyi, farkl\u0131 denemeleri kar\u015f\u0131la\u015ft\u0131rmay\u0131 ve en iyi modeli se\u00e7meyi kolayla\u015ft\u0131r\u0131r.<\/p>\n
Model E\u011fitimi ve Versiyonlama<\/h4>\n
Skyulf, model e\u011fitim kodunun ve \u00e7\u0131kt\u0131 modelinin versiyonlanmas\u0131n\u0131 otomatikle\u015ftirir.<\/p>\n
\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n# \u00d6zellikleri Skyulf Feature Store'dan y\u00fckle\nfeatures = skyulf.feature_store.load_features(\"musteri_segmentasyon_ozellikleri_v1\")\ntarget = features['churn'] # Hedef de\u011fi\u015fken\n\nX = features.drop('churn', axis=1)\ny = target\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Skyulf ile yeni bir deney ba\u015flat\nwith skyulf.start_experiment(name=\"RandomForest_Churn_Prediction\", tags=[\"classification\"]):\n # Hiperparametreleri logla\n params = {\"n_estimators\": 100, \"max_depth\": 10, \"random_state\": 42}\n skyulf.log_params(params)\n\n # Modeli e\u011fit\n model = RandomForestClassifier(**params)\n model.fit(X_train, y_train)\n\n # Tahmin yap ve metrikleri hesapla\n y_pred = model.predict(X_test)\n accuracy = accuracy_score(y_test, y_pred)\n skyulf.log_metric(\"accuracy\", accuracy)\n\n # Modeli kaydet\n skyulf.register_model(model, name=\"ChurnPredictionModel\", version=\"v1.0\", description=\"\u0130lk churn tahmin modeli\")\n<\/pre>\n<\/code><\/p>\n
Hiperparametre Optimizasyonu<\/h4>\n
Skyulf'un deney takip sistemi, farkl\u0131 hiperparametre setleri ile yap\u0131lan denemeleri kaydeder ve g\u00f6rselle\u015ftirir. Bu, en iyi performans g\u00f6steren parametre kombinasyonunu bulmay\u0131 kolayla\u015ft\u0131r\u0131r. Geli\u015ftiriciler, yerel ortamda h\u0131zl\u0131ca farkl\u0131 denemeler yapabilir ve sonu\u00e7lar\u0131 Skyulf aray\u00fcz\u00fcnden takip edebilirler.<\/p>\n
Deney Takibi ve Kar\u015f\u0131la\u015ft\u0131rma<\/h4>\n
Skyulf, her deneme i\u00e7in kaydedilen metrikleri, parametreleri ve kullan\u0131lan veri setlerini merkezi bir aray\u00fczde sunar. Bu sayede, farkl\u0131 modellerin performanslar\u0131 kolayca kar\u015f\u0131la\u015ft\u0131r\u0131labilir ve en uygun model se\u00e7imi yap\u0131labilir.<\/p>\n
Model Da\u011f\u0131t\u0131m\u0131 (Deployment) ve \u0130zleme<\/h3>\n
E\u011fitilmi\u015f ve do\u011frulanm\u0131\u015f bir modelin ger\u00e7ek d\u00fcnya uygulamalar\u0131nda kullan\u0131labilmesi i\u00e7in da\u011f\u0131t\u0131lmas\u0131 gerekir. Skyulf, yerel ve bulut da\u011f\u0131t\u0131m senaryolar\u0131n\u0131 destekleyerek bu s\u00fcreci basitle\u015ftirir.<\/p>\n
Yerel Test ve Do\u011frulama<\/h4>\n
Modeli \u00fcretim ortam\u0131na da\u011f\u0131tmadan \u00f6nce, yerel ortamda kapsaml\u0131 bir \u015fekilde test etmek \u00f6nemlidir. Skyulf, modelin bir API olarak yerel olarak \u00e7al\u0131\u015ft\u0131r\u0131lmas\u0131n\u0131 ve test edilmesini sa\u011flayan ara\u00e7lar sunar.<\/p>\n
\n# Skyulf Model Registry'den modeli y\u00fckle\nmodel = skyulf.model_registry.load_model(\"ChurnPredictionModel\", version=\"v1.0\")\n\n# Yerel bir API endpoint'i olarak modeli ba\u015flat\nskyulf.deploy_local_api(model, name=\"churn_api_local\", port=8000)\n\n# API'yi test etmek i\u00e7in istek g\u00f6nder\n# import requests\n# response = requests.post(\"http:\/\/localhost:8000\/predict\", json={\"feature1\": 10, \"feature2\": \"A\"})\n# print(response.json())\n<\/pre>\n<\/code><\/p>\n
Konteynerle\u015ftirme ve Paketleme<\/h4>\n
Tutarl\u0131 bir da\u011f\u0131t\u0131m i\u00e7in modellerin konteynerle\u015ftirilmesi kritik \u00f6neme sahiptir. Skyulf, modelleri ve ba\u011f\u0131ml\u0131l\u0131klar\u0131n\u0131 Docker konteynerlerine paketlemeyi otomatikle\u015ftirir. Bu, modelin farkl\u0131 ortamlarda ayn\u0131 \u015fekilde \u00e7al\u0131\u015fmas\u0131n\u0131 garanti eder.<\/p>\n
Da\u011f\u0131t\u0131m Stratejileri (Yerel\/Bulut)<\/h4>\n
Skyulf, modellerin hem yerel sunuculara hem de bulut platformlar\u0131na (AWS, Azure, GCP) da\u011f\u0131t\u0131m\u0131n\u0131 destekler. Geli\u015ftiriciler, modelin performans ve \u00f6l\u00e7eklenebilirlik ihtiya\u00e7lar\u0131na g\u00f6re en uygun da\u011f\u0131t\u0131m stratejisini se\u00e7ebilirler. Yerel da\u011f\u0131t\u0131m, d\u00fc\u015f\u00fck gecikmeli uygulamalar veya veri gizlili\u011fi gereksinimleri olan senaryolar i\u00e7in idealdir. Bulut da\u011f\u0131t\u0131m\u0131 ise y\u00fcksek \u00f6l\u00e7eklenebilirlik ve global eri\u015fim sa\u011flar.<\/p>\n
Model \u0130zleme ve Bak\u0131m<\/h4>\n
Da\u011f\u0131t\u0131lan modellerin performans\u0131 zamanla d\u00fc\u015febilir (model drift). Skyulf, da\u011f\u0131t\u0131lan modellerin performans\u0131n\u0131 izlemek i\u00e7in entegre ara\u00e7lar sunar. Bu ara\u00e7lar, modelin tahminlerini, gecikme s\u00fcrelerini ve veri kaymas\u0131n\u0131 takip ederek, gerekti\u011finde yeniden e\u011fitim veya g\u00fcncelleme yap\u0131lmas\u0131n\u0131 sa\u011flar.<\/p>\n
Skyulf ile Pratik Bir \u0130\u015f Ak\u0131\u015f\u0131 \u00d6rne\u011fi<\/h3>\n
Bir m\u00fc\u015fteri terk (churn) tahmini projesi \u00fczerinden Skyulf ile yerel odakl\u0131 bir MLOps i\u015f ak\u0131\u015f\u0131n\u0131 \u00f6zetleyelim:<\/p>\n
\n- Veri Al\u0131m\u0131 ve \u00d6zellik Olu\u015fturma:<\/strong>\n
\n- M\u00fc\u015fteri verileri yerel veritaban\u0131ndan \u00e7ekilir.<\/li>\n
- Skyulf'un veri versiyonlama \u00f6zelli\u011fi ile ham veri kaydedilir.<\/li>\n
- Ya\u015f gruplar\u0131, harcama kategorileri gibi \u00f6zellikler olu\u015fturulur.<\/li>\n
- Olu\u015fturulan \u00f6zellikler, Skyulf'un yerel \u00f6zellik deposuna kaydedilir ve versiyonlan\u0131r.<\/li>\n<\/ul>\n<\/li>\n
- Model E\u011fitimi ve De\u011ferlendirme:<\/strong>\n
\n- \u00d6zellik deposundan en g\u00fcncel \u00f6zellikler \u00e7ekilir.<\/li>\n
- Farkl\u0131 makine \u00f6\u011frenimi algoritmalar\u0131 (\u00f6rn. Random Forest, Gradient Boosting) ile modeller e\u011fitilir.<\/li>\n
- Skyulf'un deney takip sistemi, her modelin hiperparametrelerini, e\u011fitim metriklerini (do\u011fruluk, kesinlik, geri \u00e7a\u011f\u0131rma) ve kullan\u0131lan \u00f6zellik versiyonlar\u0131n\u0131 kaydeder.<\/li>\n
- En iyi performans g\u00f6steren model, Skyulf'un model kay\u0131t defterine kaydedilir.<\/li>\n<\/ul>\n<\/li>\n
- Model Da\u011f\u0131t\u0131m\u0131 ve API Olu\u015fturma:<\/strong>\n
\n- Kay\u0131t defterinden se\u00e7ilen model, Skyulf'un da\u011f\u0131t\u0131m orkestrat\u00f6r\u00fc arac\u0131l\u0131\u011f\u0131yla Docker konteynerine paketlenir.<\/li>\n
- Model, bir REST API olarak yerel bir sunucuya veya \u015firket i\u00e7i Kubernetes k\u00fcmesine da\u011f\u0131t\u0131l\u0131r.<\/li>\n
- Skyulf'un izleme mod\u00fcl\u00fc, da\u011f\u0131t\u0131lan API'nin gecikme s\u00fcresini, hata oranlar\u0131n\u0131 ve tahmin da\u011f\u0131l\u0131mlar\u0131n\u0131 takip eder.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n
Bu i\u015f ak\u0131\u015f\u0131, geli\u015ftiricilerin yerel ortamda h\u0131zl\u0131ca iterasyon yapmas\u0131n\u0131, bulut maliyetlerini optimize etmesini ve hassas verileri g\u00fcvende tutmas\u0131n\u0131 sa\u011flar.<\/p>\n
Sonu\u00e7<\/h3>\n
Makine \u00f6\u011frenimi projelerinin karma\u015f\u0131kl\u0131\u011f\u0131 artt\u0131k\u00e7a, MLOps pratikleri vazge\u00e7ilmez hale gelmektedir. Skyulf gibi yerel odakl\u0131 bir MLOps platformu, \u00f6zellikle geli\u015ftirme ve ilk da\u011f\u0131t\u0131m a\u015famalar\u0131nda ekiplere \u00f6nemli avantajlar sunar. H\u0131z, maliyet verimlili\u011fi, veri g\u00fcvenli\u011fi ve \u00e7eviklik gibi faydalar\u0131yla Skyulf, \u00f6zellik m\u00fchendisli\u011finden model da\u011f\u0131t\u0131m\u0131na kadar t\u00fcm s\u00fcreci standardize ederek, makine \u00f6\u011frenimi modellerinin \u00fcretim ortam\u0131na daha h\u0131zl\u0131 ve g\u00fcvenilir bir \u015fekilde ula\u015fmas\u0131n\u0131 sa\u011flar. Bu yakla\u015f\u0131m, modern MLOps stratejilerinde \u00f6nemli bir yer edinmeye adayd\u0131r.<\/p>\n
SSS (S\u0131k Sorulan Sorular)<\/h3>\n1. Skyulf sadece yerel ortamda m\u0131 \u00e7al\u0131\u015f\u0131r?<\/h4>\n
Hay\u0131r, Skyulf yerel odakl\u0131 bir platform olsa da, e\u011fitilmi\u015f modellerin bulut ortamlar\u0131na (AWS, Azure, GCP) da\u011f\u0131t\u0131m\u0131n\u0131 da destekler. Geli\u015ftirme ve test s\u00fcre\u00e7leri yerelde tutularak maliyet ve h\u0131z avantaj\u0131 sa\u011flan\u0131r, ancak \u00f6l\u00e7eklenebilirlik gerekti\u011finde buluta ge\u00e7i\u015f kolayd\u0131r.<\/p>\n
2. Skyulf hangi makine \u00f6\u011frenimi k\u00fct\u00fcphanelerini destekler?<\/h4>\n
Skyulf, pop\u00fcler makine \u00f6\u011frenimi k\u00fct\u00fcphaneleri olan Scikit-learn, TensorFlow, PyTorch, XGBoost gibi k\u00fct\u00fcphanelerle uyumlu \u00e7al\u0131\u015facak \u015fekilde tasarlanm\u0131\u015ft\u0131r. Model format\u0131 ba\u011f\u0131ms\u0131zl\u0131\u011f\u0131 sayesinde geni\u015f bir yelpazede model t\u00fcrlerini destekler.<\/p>\n
3. Skyulf'un veri versiyonlama \u00f6zelli\u011fi nas\u0131l \u00e7al\u0131\u015f\u0131r?<\/h4>\n
Skyulf, veri setlerinin anl\u0131k g\u00f6r\u00fcnt\u00fclerini al\u0131r ve bu anl\u0131k g\u00f6r\u00fcnt\u00fcleri meta verileriyle birlikte saklar. Bu sayede, bir modelin hangi veri seti versiyonuyla e\u011fitildi\u011fi net bir \u015fekilde izlenebilir ve ge\u00e7mi\u015fteki herhangi bir veri setine geri d\u00f6n\u00fclebilir.<\/p>\n
4. \u00d6zellik deposu (Feature Store) neden \u00f6nemlidir?<\/h4>\n
\u00d6zellik deposu, veri bilimcilerinin ve m\u00fchendislerinin farkl\u0131 modeller i\u00e7in ayn\u0131 tutarl\u0131 ve versiyonlanm\u0131\u015f \u00f6zellikleri kullanmas\u0131n\u0131 sa\u011flar. Bu, \u00f6zellik yeniden kullan\u0131m\u0131n\u0131 te\u015fvik eder, tutars\u0131zl\u0131klar\u0131 azalt\u0131r ve model geli\u015ftirme s\u00fcrecini h\u0131zland\u0131r\u0131r.<\/p>\n
5. Skyulf, CI\/CD s\u00fcre\u00e7leriyle nas\u0131l entegre olur?<\/h4>\n
Skyulf, g\u00fc\u00e7l\u00fc bir API ve CLI sunarak mevcut CI\/CD boru hatlar\u0131na kolayca entegre edilebilir. Bu sayede, kod de\u011fi\u015fiklikleri veya yeni veri setleri alg\u0131land\u0131\u011f\u0131nda otomatik model e\u011fitimi, test ve da\u011f\u0131t\u0131m tetiklenebilir.<\/p>\n","protected":false},"excerpt":{"rendered":"\u00d6zellik M\u00fchendisli\u011finden Da\u011f\u0131t\u0131ma: Skyulf ile Yerel Odakl\u0131 Bir MLOps \u0130\u015f Ak\u0131\u015f\u0131\nMakine \u00f6\u011frenimi projelerinin ba\u015far\u0131ya ula\u015fmas\u0131nda sadece iyi…","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-36812","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-genel","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\n
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