{"id":41457,"date":"2026-04-27T14:03:46","date_gmt":"2026-04-27T11:03:46","guid":{"rendered":"https:\/\/fatihsoysal.com\/blog\/mongodb-ile-model-sonrasi-veri-saklama-ve-sorgulama-yapay-zeka-ciktilarini-yonetme-rehberi\/"},"modified":"2026-04-27T14:03:46","modified_gmt":"2026-04-27T11:03:46","slug":"mongodb-ile-model-sonrasi-veri-saklama-ve-sorgulama-yapay-zeka-ciktilarini-yonetme-rehberi","status":"publish","type":"post","link":"https:\/\/fatihsoysal.com\/blog\/mongodb-ile-model-sonrasi-veri-saklama-ve-sorgulama-yapay-zeka-ciktilarini-yonetme-rehberi\/","title":{"rendered":"MongoDB ile Model Sonras\u0131 Veri Saklama ve Sorgulama: Yapay Zeka \u00c7\u0131kt\u0131lar\u0131n\u0131 Y\u00f6netme Rehberi"},"content":{"rendered":"
<title>MongoDB ile Model Sonras\u0131 Veri Saklama ve Sorgulama<\/title>\n<meta name="description" content="Makine \u00f6\u011frenmesi modellerinizin \u00e7\u0131kt\u0131s\u0131n\u0131 MongoDB'de verimli bir \u015fekilde saklay\u0131n ve sorgulay\u0131n. Ger\u00e7ek d\u00fcnya senaryolar\u0131 ve ad\u0131m ad\u0131m rehberle \u00f6\u011frenin.">\n\n<h2>MongoDB ile Model Sonras\u0131 Veri Saklama ve Sorgulama: Yapay Zeka \u00c7\u0131kt\u0131lar\u0131n\u0131 Y\u00f6netme Rehberi<\/h2>\n\nYapay zeka ve makine \u00f6\u011frenmesi modelleri hayat\u0131m\u0131z\u0131n her alan\u0131nda giderek daha fazla yer kapl\u0131yor. Bu modellerin \u00fcretti\u011fi \u00e7\u0131kt\u0131lar, i\u015fletmeler i\u00e7in paha bi\u00e7ilmez bilgiler sunabilir. Ancak, bu \u00e7\u0131kt\u0131lar\u0131 etkili bir \u015fekilde depolamak, y\u00f6netmek ve sorgulamak, modelin ger\u00e7ek de\u011ferini ortaya \u00e7\u0131karmak i\u00e7in kritik \u00f6neme sahiptir. \u0130\u015fte tam bu noktada, esnek ve \u00f6l\u00e7eklenebilir yap\u0131s\u0131yla MongoDB, model sonras\u0131 verileriniz i\u00e7in g\u00fc\u00e7l\u00fc bir \u00e7\u00f6z\u00fcm sunar. Bu makalede, MongoDB'yi kullanarak yapay zeka ve makine \u00f6\u011frenmesi modellerinizin \u00fcretti\u011fi sonu\u00e7lar\u0131 nas\u0131l depolayabilece\u011finizi, bu verileri nas\u0131l verimli bir \u015fekilde sorgulayabilece\u011finizi ve ger\u00e7ek d\u00fcnya senaryolar\u0131nda bu teknolojiyi nas\u0131l kullanabilece\u011finizi derinlemesine inceleyece\u011fiz. Amac\u0131m\u0131z, bu konuya tamamen yabanc\u0131 olanlar\u0131n bile anlayabilece\u011fi \u015fekilde, ad\u0131m ad\u0131m ilerleyerek kapsaml\u0131 bir rehber sunmakt\u0131r.\n\n<h2>Neden Model Sonras\u0131 Verileri Y\u00f6netmek \u00d6nemli?<\/h2>\n\nMakine \u00f6\u011frenmesi modelleri, tahminler, s\u0131n\u0131fland\u0131rmalar, \u00f6neriler veya anormallik tespitleri gibi \u00e7e\u015fitli \u00e7\u0131kt\u0131lar \u00fcretir. Bu \u00e7\u0131kt\u0131lar, bir e-ticaret sitesinde ki\u015fiselle\u015ftirilmi\u015f \u00fcr\u00fcn \u00f6nerilerinden, bir sa\u011fl\u0131k uygulamas\u0131nda hasta risk skorlar\u0131na kadar geni\u015f bir yelpazede kullan\u0131labilir. Ancak, bu ham \u00e7\u0131kt\u0131lar tek ba\u015flar\u0131na yeterince anlaml\u0131 olmayabilir. Bu verileri, orijinal girdi verileriyle birlikte veya ek meta verilerle birlikte saklamak, a\u015fa\u011f\u0131daki nedenlerle b\u00fcy\u00fck \u00f6nem ta\u015f\u0131r:\n\n<ul>\n    <li><strong>Model Performans\u0131n\u0131 \u0130zleme ve De\u011ferlendirme:<\/strong> Zaman i\u00e7inde modelin performans\u0131n\u0131 takip etmek, hatalar\u0131 tespit etmek ve iyile\u015ftirme alanlar\u0131n\u0131 belirlemek i\u00e7in ge\u00e7mi\u015f \u00e7\u0131kt\u0131lara ihtiya\u00e7 duyar\u0131z.<\/li>\n    <li><strong>Daha Derin Analizler:<\/strong> Saklanan \u00e7\u0131kt\u0131lar, ek analizler yaparak trendleri anlamak, m\u00fc\u015fteri davran\u0131\u015flar\u0131n\u0131 \u00e7\u00f6zmek veya operasyonel verimlili\u011fi art\u0131rmak i\u00e7in kullan\u0131labilir.<\/li>\n    <li><strong>Yeniden E\u011fitim ve \u0130yile\u015ftirme:<\/strong> Yeni veriler geldik\u00e7e veya model g\u00fcncellendik\u00e7e, ge\u00e7mi\u015f verilerle kar\u015f\u0131la\u015ft\u0131rma yapmak ve modeli yeniden e\u011fitmek i\u00e7in saklanan \u00e7\u0131kt\u0131lar gereklidir.<\/li>\n    <li><strong>\u0130\u015f Kararlar\u0131 Alma:<\/strong> \u00dcretilen \u00e7\u0131kt\u0131lar, do\u011frudan i\u015f kararlar\u0131n\u0131 etkileyebilir. \u00d6rne\u011fin, bir doland\u0131r\u0131c\u0131l\u0131k tespit modelinin \u00e7\u0131kt\u0131s\u0131, bir i\u015flemin onaylan\u0131p onaylanmayaca\u011f\u0131n\u0131 belirleyebilir.<\/li>\n    <li><strong>Uyumluluk ve Denetim:<\/strong> Baz\u0131 sekt\u00f6rlerde, modelin ald\u0131\u011f\u0131 kararlar\u0131n ve \u00fcretti\u011fi \u00e7\u0131kt\u0131lar\u0131n kaydedilmesi yasal zorunluluk olabilir.<\/li>\n<\/ul>\n\nBu veri y\u0131\u011f\u0131nlar\u0131n\u0131 y\u00f6netmenin geleneksel ili\u015fkisel veritabanlar\u0131nda zorlay\u0131c\u0131 olabilece\u011fini d\u00fc\u015f\u00fcnebilirsiniz. \u0130li\u015fkisel veritabanlar\u0131 genellikle sabit \u015femalara dayan\u0131r ve de\u011fi\u015fen veya yap\u0131land\u0131r\u0131lmam\u0131\u015f verilerle ba\u015fa \u00e7\u0131kmakta zorlanabilir. \u0130\u015fte bu noktada, MongoDB'nin esnek dok\u00fcman tabanl\u0131 yap\u0131s\u0131 devreye girer.\n\n<h2>MongoDB'nin Temel Kavramlar\u0131 ve Yapay Zeka Verileri \u0130\u00e7in Uygunlu\u011fu<\/h2>\n\nMongoDB, NoSQL veritaban\u0131 ailesinin bir \u00fcyesidir ve \u00f6zellikle esnek veri modelleriyle tan\u0131n\u0131r. Geleneksel ili\u015fkisel veritabanlar\u0131n\u0131n aksine, MongoDB verileri JSON benzeri BSON (Binary JSON) format\u0131nda saklar. Bu, her bir verinin kendi yap\u0131s\u0131n\u0131 ta\u015f\u0131yabilece\u011fi anlam\u0131na gelir.\n\n<h3>BSON Dok\u00fcmanlar\u0131 ve \u015eema Esnekli\u011fi<\/h3>\n\nMongoDB'deki temel veri birimi "dok\u00fcman"d\u0131r. Bir dok\u00fcman, anahtar-de\u011fer \u00e7iftlerinden olu\u015fan bir koleksiyondur. Bu anahtar-de\u011fer \u00e7iftleri, basit veri t\u00fcrlerinden (string, integer, boolean) daha karma\u015f\u0131k yap\u0131lara (diziler, g\u00f6m\u00fcl\u00fc dok\u00fcmanlar) kadar her \u015feyi i\u00e7erebilir.\n\n\u00d6rne\u011fin, bir g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma modelinin \u00e7\u0131kt\u0131s\u0131n\u0131 saklamak istedi\u011fimizi varsayal\u0131m. Geleneksel bir veritaban\u0131nda, her bir \u00f6zellik i\u00e7in ayr\u0131 s\u00fctunlar tan\u0131mlaman\u0131z gerekirdi. Ancak MongoDB'de, tek bir dok\u00fcmanda hem girdi g\u00f6r\u00fcnt\u00fcs\u00fcn\u00fcn kimli\u011fi, hem modelin tahmin etti\u011fi s\u0131n\u0131f, hem tahminin olas\u0131l\u0131\u011f\u0131, hem de ek meta veriler (\u00f6rne\u011fin, tahminin yap\u0131ld\u0131\u011f\u0131 zaman, modelin s\u00fcr\u00fcm\u00fc) gibi bilgileri saklayabilirsiniz.<\/code><\/pre>\n
json
\n{
\n  “_id”: ObjectId(“60f7b3b3b3b3b3b3b3b3b3b3”),
\n  “image_id”: “img_12345”,
\n  “prediction”: {
\n    “class”: “cat”,
\n    “probability”: 0.95
\n  },
\n  “timestamp”: ISODate(“2023-10-27T10:00:00Z”),
\n  “model_version”: “v1.2”,
\n  “metadata”: {
\n    “source”: “web_upload”,
\n    “user_id”: “user_abc”
\n  }
\n}<\/p>\n
Bu esneklik, model \u00e7\u0131kt\u0131lar\u0131n\u0131n yap\u0131s\u0131 zamanla de\u011fi\u015fse bile veritaban\u0131 \u015femas\u0131n\u0131 s\u00fcrekli olarak g\u00fcncelleme ihtiyac\u0131n\u0131 ortadan kald\u0131r\u0131r. Bu, \u00f6zellikle s\u00fcrekli geli\u015fen yapay zeka modelleriyle \u00e7al\u0131\u015f\u0131rken b\u00fcy\u00fck bir avantajd\u0131r.\n\n<h3>Koleksiyonlar ve Veri Gruplama<\/h3>\n\nMongoDB'de dok\u00fcmanlar "koleksiyonlar" i\u00e7inde grupland\u0131r\u0131l\u0131r. Bir koleksiyon, benzer yap\u0131daki dok\u00fcmanlar\u0131 bar\u0131nd\u0131r\u0131r. \u00d6rne\u011fin, g\u00f6r\u00fcnt\u00fc s\u0131n\u0131fland\u0131rma modellerinin \u00e7\u0131kt\u0131lar\u0131 i\u00e7in bir "image_predictions" koleksiyonu olu\u015fturabilirsiniz. E\u011fer farkl\u0131 t\u00fcrde modelleriniz varsa (\u00f6rne\u011fin, metin s\u0131n\u0131fland\u0131rma, nesne tespiti), her biri i\u00e7in ayr\u0131 koleksiyonlar veya daha karma\u015f\u0131k bir yap\u0131land\u0131rma kullanabilirsiniz.\n\n<h3>\u0130ndeksleme ve Sorgulama Performans\u0131<\/h3>\n\nMongoDB'nin en g\u00fc\u00e7l\u00fc yanlar\u0131ndan biri, verileri h\u0131zl\u0131 bir \u015fekilde sorgulama yetene\u011fidir. Bunu, "indeksler" arac\u0131l\u0131\u011f\u0131yla ba\u015far\u0131r. \u0130ndeksler, veritaban\u0131n\u0131n belirli alanlara g\u00f6re verileri daha h\u0131zl\u0131 bulmas\u0131n\u0131 sa\u011flayan \u00f6zel veri yap\u0131lar\u0131d\u0131r. Model \u00e7\u0131kt\u0131lar\u0131nda s\u0131k\u00e7a sorgulanan alanlar (\u00f6rne\u011fin, image_id<\/code>, timestamp<\/code>, prediction.class<\/code>) \u00fczerine indeksler olu\u015fturarak sorgu performans\u0131n\u0131 \u00f6nemli \u00f6l\u00e7\u00fcde art\u0131rabilirsiniz.\n\n<h2>Ad\u0131m Ad\u0131m: Model \u00c7\u0131kt\u0131lar\u0131n\u0131 MongoDB'ye Kaydetme<\/h2>\n\nModel \u00e7\u0131kt\u0131s\u0131n\u0131 MongoDB'ye kaydetmek, genellikle modelin \u00e7al\u0131\u015ft\u0131\u011f\u0131 kodun bir par\u00e7as\u0131 olarak ger\u00e7ekle\u015ftirilir. Bu s\u00fcre\u00e7, kulland\u0131\u011f\u0131n\u0131z programlama diline ve MongoDB s\u00fcr\u00fcc\u00fcs\u00fcne ba\u011fl\u0131 olarak de\u011fi\u015fiklik g\u00f6sterebilir. Burada Python kullanarak temel bir \u00f6rnek sunaca\u011f\u0131z.\n\n\u00d6ncelikle, MongoDB'ye ba\u011flanmak i\u00e7in gerekli k\u00fct\u00fcphaneyi y\u00fcklemeniz gerekir:<\/code><\/pre>\n
bash
\npip install pymongo<\/p>\n
Ard\u0131ndan, MongoDB'ye ba\u011flan\u0131p verileri ekleyebilirsiniz:<\/code><\/pre>\n
python
\nfrom pymongo import MongoClient
\nfrom datetime import datetime<\/p>\n
# MongoDB’ye ba\u011flanma (varsay\u0131lan host ve port ile)
\nclient = MongoClient(‘mongodb:\/\/localhost:27017\/’)
\ndb = client[‘ai_outputs_db’]  # Veritaban\u0131 ad\u0131
\ncollection = db[‘image_predictions’] # Koleksiyon ad\u0131<\/p>\n
# Model \u00e7\u0131kt\u0131s\u0131n\u0131 temsil eden bir Python s\u00f6zl\u00fc\u011f\u00fc
\nmodel_output = {
\n    “image_id”: “img_67890”,
\n    “prediction”: {
\n        “class”: “dog”,
\n        “probability”: 0.88
\n    },
\n    “timestamp”: datetime.utcnow(), # UTC zaman damgas\u0131
\n    “model_version”: “v1.3”,
\n    “metadata”: {
\n        “source”: “api_call”,
\n        “request_id”: “req_xyz”
\n    }
\n}<\/p>\n
# Dok\u00fcman\u0131 koleksiyona ekleme
\ninsert_result = collection.insert_one(model_output)<\/p>\n
print(f”Dok\u00fcman ba\u015far\u0131yla eklendi. ID: {insert_result.inserted_id}”)<\/p>\n
# Ba\u011flant\u0131y\u0131 kapatma (genellikle uygulama sonland\u0131\u011f\u0131nda otomatik olur, ancak a\u00e7\u0131k\u00e7a yapmak da iyi bir pratiktir)
\nclient.close()<\/p>\n
Bu basit kod par\u00e7as\u0131, bir modelin \u00fcretti\u011fi \u00e7\u0131kt\u0131y\u0131 bir Python s\u00f6zl\u00fc\u011f\u00fc olarak haz\u0131rlay\u0131p, ard\u0131ndan bu s\u00f6zl\u00fc\u011f\u00fc MongoDB'deki belirtilen koleksiyona bir dok\u00fcman olarak ekler. datetime.utcnow()<\/code> ile eklenen zaman damgas\u0131, verinin ne zaman \u00fcretildi\u011fini takip etmek i\u00e7in \u00f6nemlidir.\n\n<h3>G\u00f6m\u00fcl\u00fc Dok\u00fcmanlar ve Dizilerle Zenginle\u015ftirme<\/h3>\n\nModel \u00e7\u0131kt\u0131s\u0131, birden fazla tahmin veya daha karma\u015f\u0131k bilgiler i\u00e7erebilir. MongoDB'nin g\u00f6m\u00fcl\u00fc dok\u00fcmanlar ve diziler deste\u011fi, bu t\u00fcr yap\u0131lar\u0131 do\u011fal bir \u015fekilde modellemenizi sa\u011flar.\n\n\u00d6rne\u011fin, bir nesne tespit modelinin \u00e7\u0131kt\u0131s\u0131, birden fazla nesne ve her bir nesne i\u00e7in s\u0131n\u0131rlay\u0131c\u0131 kutu bilgisi i\u00e7erebilir.<\/code><\/pre>\n
json
\n{
\n  “_id”: ObjectId(“60f7b3b3b3b3b3b3b3b3b3b4”),
\n  “image_id”: “img_abcde”,
\n  “detections”: [
\n    {
\n      “label”: “car”,
\n      “confidence”: 0.92,
\n      “bounding_box”: {“x_min”: 100, “y_min”: 150, “x_max”: 300, “y_max”: 250}
\n    },
\n    {
\n      “label”: “person”,
\n      “confidence”: 0.75,
\n      “bounding_box”: {“x_min”: 200, “y_min”: 100, “x_max”: 250, “y_max”: 300}
\n    }
\n  ],
\n  “timestamp”: ISODate(“2023-10-27T11:00:00Z”),
\n  “model_version”: “v2.0”
\n}<\/p>\n
Bu yap\u0131, her bir nesne i\u00e7in ayr\u0131 bir tablo gerektiren ili\u015fkisel bir veritaban\u0131na g\u00f6re \u00e7ok daha kompaktt\u0131r ve okunmas\u0131 daha kolayd\u0131r.\n\n<h2>Model Sonras\u0131 Verileri Sorgulama: G\u00fc\u00e7l\u00fc Sorgu Mekanizmalar\u0131<\/h2>\n\nMongoDB'nin esnek sorgu dilini kullanarak, depolad\u0131\u011f\u0131n\u0131z model \u00e7\u0131kt\u0131lar\u0131n\u0131 \u00e7e\u015fitli \u015fekillerde filtreleyebilir, s\u0131ralayabilir ve analiz edebilirsiniz.\n\n<h3>Temel Sorgular: Belirli Kriterlere G\u00f6re Veri Bulma<\/h3>\n\nEn basit sorgular, belirli alanlar\u0131n belirli de\u011ferlere e\u015fit oldu\u011fu dok\u00fcmanlar\u0131 bulmay\u0131 i\u00e7erir.\n\nPython ile bir \u00f6rnek: Belirli bir image_id<\/code>'ye ait t\u00fcm tahminleri bulma.<\/code><\/pre>\n
python
\nfrom pymongo import MongoClient<\/p>\n
client = MongoClient(‘mongodb:\/\/localhost:27017\/’)
\ndb = client[‘ai_outputs_db’]
\ncollection = db[‘image_predictions’]<\/p>\n
image_id_to_find = “img_12345”<\/p>\n
# Sorgu ko\u015fulu
\nquery = {“image_id”: image_id_to_find}<\/p>\n
# Sorguyu \u00e7al\u0131\u015ft\u0131rma ve sonu\u00e7lar\u0131 alma
\nresults = collection.find(query)<\/p>\n
print(f”‘{image_id_to_find}’ ID’sine ait tahminler:”)
\nfor doc in results:
\n    print(doc)<\/p>\n
client.close()<\/p>\n
Bu sorgu, image_id<\/code> alan\u0131 img_12345<\/code> olan t\u00fcm dok\u00fcmanlar\u0131 d\u00f6nd\u00fcr\u00fcr.\n\n<h3>G\u00f6m\u00fcl\u00fc Alanlara G\u00f6re Sorgulama<\/h3>\n\nG\u00f6m\u00fcl\u00fc dok\u00fcmanlardaki alanlara eri\u015fmek i\u00e7in nokta (.) notasyonunu kullanabilirsiniz.\n\n\u00d6rnek: Belirli bir s\u0131n\u0131f\u0131n tahmin olas\u0131l\u0131\u011f\u0131 belirli bir e\u015fi\u011fin \u00fczerinde olan t\u00fcm tahminleri bulma.<\/code><\/pre>\n
python
\nquery = {“prediction.class”: “cat”, “prediction.probability”: {“$gt”: 0.9}}
\nresults = collection.find(query)<\/p>\n
print(“\\nOlas\u0131l\u0131\u011f\u0131 0.9’un \u00fczerinde olan ‘cat’ tahminleri:”)
\nfor doc in results:
\n    print(doc)<\/p>\n
Burada $gt<\/code> operat\u00f6r\u00fc, "greater than" (b\u00fcy\u00fckt\u00fcr) anlam\u0131na gelir. MongoDB, \u00e7e\u015fitli sorgu operat\u00f6rleri sunar ($lt<\/code>, $eq<\/code>, $ne<\/code>, $in<\/code>, $nin<\/code> vb.).\n\n<h3>Zaman Aral\u0131\u011f\u0131na G\u00f6re Sorgulama<\/h3>\n\nZaman damgalar\u0131n\u0131 kullanarak belirli bir zaman dilimindeki \u00e7\u0131kt\u0131lar\u0131 sorgulamak olduk\u00e7a yayg\u0131nd\u0131r.\n\n\u00d6rnek: Belirli bir g\u00fcn i\u00e7inde yap\u0131lan t\u00fcm tahminleri bulma.<\/code><\/pre>\n
python
\nfrom datetime import datetime, timedelta<\/p>\n
# Sorgulanacak g\u00fcn\u00fcn ba\u015flang\u0131\u00e7 ve biti\u015f zaman\u0131
\nstart_date = datetime(2023, 10, 27, 0, 0, 0)
\nend_date = datetime(2023, 10, 27, 23, 59, 59)<\/p>\n
query = {
\n    “timestamp”: {
\n        “$gte”: start_date, # Greater than or equal to
\n        “$lte”: end_date    # Less than or equal to
\n    }
\n}<\/p>\n
results = collection.find(query)<\/p>\n
print(f”\\n{start_date.date()} tarihine ait tahminler:”)
\nfor doc in results:
\n    print(doc)<\/p>\n
Bu sorgu, timestamp<\/code> alan\u0131 belirtilen start_date<\/code> ve end_date<\/code> aras\u0131nda olan t\u00fcm dok\u00fcmanlar\u0131 getirir.\n\n<h3>\u0130ndeks Olu\u015fturman\u0131n \u00d6nemi<\/h3>\n\nYukar\u0131daki sorgular\u0131n performans\u0131n\u0131 art\u0131rmak i\u00e7in, s\u0131k\u00e7a sorgulanan alanlara indeksler eklemek kritiktir.\n\n\u00d6rnek: image_id<\/code> alan\u0131na bir indeks olu\u015fturma.<\/code><\/pre>\n
python
\n# ‘image_id’ alan\u0131na bir indeks olu\u015fturma (tek alanl\u0131 indeks)
\ncollection.create_index(“image_id”)<\/p>\n
# G\u00f6m\u00fcl\u00fc alanlara da indeks olu\u015fturulabilir
\ncollection.create_index(“prediction.class”)<\/p>\n
# Birden fazla alana birlikte indeks olu\u015fturma (bile\u015fik indeks)
\ncollection.create_index([(“timestamp”, 1), (“prediction.class”, -1)]) # 1: artan, -1: azalan s\u0131ralama<\/p>\n
\u0130ndeksler, sorgu s\u00fcresini milisaniyelerden mikrosaniyelere indirebilir, bu da b\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken hayati \u00f6nem ta\u015f\u0131r.\n\n<h2>Ger\u00e7ek D\u00fcnya Senaryolar\u0131 ve Vaka Analizleri<\/h2>\n\nModel sonras\u0131 verilerin MongoDB ile y\u00f6netilmesinin pratik faydalar\u0131n\u0131 somut \u00f6rneklerle g\u00f6relim.\n\n<h3>Vaka Analizi 1: E-Ticaret Ki\u015fiselle\u015ftirme Motoru<\/h3>\n\nBir e-ticaret platformunda, kullan\u0131c\u0131lar\u0131n ge\u00e7mi\u015f sat\u0131n alma davran\u0131\u015flar\u0131na, gezindikleri \u00fcr\u00fcnlere ve demografik bilgilerine dayanarak ki\u015fiselle\u015ftirilmi\u015f \u00fcr\u00fcn \u00f6nerileri sunan bir makine \u00f6\u011frenmesi modeli \u00e7al\u0131\u015f\u0131yor. Modelin \u00fcretti\u011fi her bir \u00f6neri (hangi \u00fcr\u00fcn\u00fcn hangi kullan\u0131c\u0131ya \u00f6nerildi\u011fi, \u00f6nerinin t\u0131klanma olas\u0131l\u0131\u011f\u0131, \u00f6nerinin yap\u0131ld\u0131\u011f\u0131 zaman vb.) MongoDB'de saklan\u0131yor.\n\n*   Saklanan Veri:<\/strong>\n    *   user_id<\/code>: Kullan\u0131c\u0131 kimli\u011fi\n    *   product_id<\/code>: \u00d6nerilen \u00fcr\u00fcn kimli\u011fi\n    *   recommendation_score<\/code>: \u00d6neri puan\u0131\n    *   recommendation_type<\/code>: (\u00f6rne\u011fin, "benzer \u00fcr\u00fcnler", "birlikte al\u0131nanlar")\n    *   timestamp<\/code>: \u00d6nerinin yap\u0131ld\u0131\u011f\u0131 zaman\n    *   model_version<\/code>: Kullan\u0131lan modelin s\u00fcr\u00fcm\u00fc\n    *   user_interaction<\/code>: (\u00f6rne\u011fin, t\u0131klama, sepete ekleme, sat\u0131n alma - bu alanlar zamanla g\u00fcncellenebilir)\n\n*   MongoDB Kullan\u0131m\u0131:<\/strong>\n    *   Yeni \u00f6neriler h\u0131zl\u0131ca eklenir.\n    *   Kullan\u0131c\u0131ya \u00f6zel \u00f6neriler, user_id<\/code>'ye g\u00f6re h\u0131zl\u0131ca sorgulan\u0131r.\n    *   Modelin performans\u0131, belirli bir zaman aral\u0131\u011f\u0131ndaki t\u0131klanma oranlar\u0131 gibi metriklerle izlenir.\n    *   A\/B testleri i\u00e7in farkl\u0131 model_version<\/code>'lara ait \u00f6neriler kar\u015f\u0131la\u015ft\u0131r\u0131l\u0131r.\n    *   user_interaction<\/code> alan\u0131n\u0131n g\u00fcncellenmesi, modelin yeniden e\u011fitilmesi i\u00e7in de\u011ferli geri bildirim sa\u011flar.\n\nBu senaryoda MongoDB'nin esnek \u015femas\u0131, yeni \u00f6neri t\u00fcrleri eklendik\u00e7e veya modelin \u00e7\u0131k\u0131\u015f yap\u0131s\u0131 g\u00fcncellendik\u00e7e veritaban\u0131 yap\u0131s\u0131n\u0131 de\u011fi\u015ftirmeden uyum sa\u011flamay\u0131 kolayla\u015ft\u0131r\u0131r.\n\n<h3>Vaka Analizi 2: Siber G\u00fcvenlik Anomali Tespiti<\/h3>\n\nBir finansal kurum, a\u011f trafi\u011fini analiz ederek \u015f\u00fcpheli aktiviteleri ve potansiyel siber sald\u0131r\u0131lar\u0131 tespit eden bir anomali tespit modeli kullan\u0131yor. Model, her bir a\u011f ba\u011flant\u0131s\u0131 veya i\u015flem i\u00e7in bir risk skoru \u00fcretiyor.\n\n*   Saklanan Veri:<\/strong>\n    *   transaction_id<\/code>: \u0130\u015flem kimli\u011fi\n    *   source_ip<\/code>, destination_ip<\/code>: IP adresleri\n    *   port<\/code>: Port numaras\u0131\n    *   anomaly_score<\/code>: Anomali riski skoru (0-1 aras\u0131)\n    *   anomaly_type<\/code>: Tespit edilen anomali t\u00fcr\u00fc (\u00f6rne\u011fin, "DDoS", "Yetkisiz Eri\u015fim Giri\u015fimi")\n    *   timestamp<\/code>: Aktivitenin ger\u00e7ekle\u015fti\u011fi zaman\n    *   model_version<\/code>: Anomali tespit modelinin s\u00fcr\u00fcm\u00fc\n    *   action_taken<\/code>: (\u00f6rne\u011fin, "engellendi", "bildirildi", "g\u00f6zlem alt\u0131nda")\n\n*   MongoDB Kullan\u0131m\u0131:<\/strong>\n    *   Y\u00fcksek riskli anomali tespitleri, anomaly_score<\/code> ve anomaly_type<\/code> \u00fczerine sorgularla an\u0131nda filtrelenir.\n    *   Belirli bir IP adresinden gelen t\u00fcm \u015f\u00fcpheli aktiviteler h\u0131zla bulunur.\n    *   Zaman i\u00e7indeki anomali trendleri, timestamp<\/code> alan\u0131na g\u00f6re analiz edilir.\n    *   G\u00fcvenlik analistlerinin incelemesi i\u00e7in belirli transaction_id<\/code>'lere ait detayl\u0131 kay\u0131tlar \u00e7ekilir.\n    *   action_taken<\/code> alan\u0131n\u0131n g\u00fcncellenmesi, otomatik m\u00fcdahale sistemlerinin geri bildirimini izlemek i\u00e7in kullan\u0131l\u0131r.\n\nBu durumda, b\u00fcy\u00fck hacimli a\u011f verilerini h\u0131zl\u0131 bir \u015fekilde i\u015flemek ve sorgulamak kritik \u00f6neme sahiptir. MongoDB'nin \u00f6l\u00e7eklenebilirli\u011fi ve performansl\u0131 sorgu yetenekleri, bu ihtiyac\u0131 kar\u015f\u0131lar.\n\n<h2>\u0130leri D\u00fczey Konular ve Optimizasyon \u0130pu\u00e7lar\u0131<\/h2>\n\nMongoDB'nin sundu\u011fu geli\u015fmi\u015f \u00f6zelliklerden yararlanarak model sonras\u0131 verilerinizi daha da etkili y\u00f6netebilirsiniz.\n\n<h3>Agregasyon \u00c7er\u00e7evesi (Aggregation Framework)<\/h3>\n\nMongoDB'nin agregasyon \u00e7er\u00e7evesi, karma\u015f\u0131k veri i\u015fleme ve analiz g\u00f6revleri i\u00e7in g\u00fc\u00e7l\u00fc bir ara\u00e7t\u0131r. Gruplama, filtreleme, d\u00f6n\u00fc\u015ft\u00fcrme ve birle\u015ftirme gibi i\u015flemleri tek bir sorguda ger\u00e7ekle\u015ftirebilirsiniz.\n\n\u00d6rne\u011fin, her bir model_version<\/code> i\u00e7in ortalama probability<\/code> de\u011ferini ve toplam tahmin say\u0131s\u0131n\u0131 hesaplamak isteyebilirsiniz:<\/code><\/pre>\n
python
\npipeline = [
\n    {
\n        “$group”: {
\n            “_id”: “$model_version”,
\n            “average_probability”: {“$avg”: “$prediction.probability”},
\n            “total_predictions”: {“$sum”: 1}
\n        }
\n    },
\n    {
\n        “$sort”: {“_id”: 1} # Model s\u00fcr\u00fcm\u00fcne g\u00f6re s\u0131rala
\n    }
\n]<\/p>\n
results = collection.aggregate(pipeline)<\/p>\n
print(“\\nModel s\u00fcr\u00fcmlerine g\u00f6re \u00f6zet istatistikler:”)
\nfor doc in results:
\n    print(doc)<\/p>\n
Bu t\u00fcr agregasyonlar, model performans\u0131n\u0131 \u00f6zetlemek ve trendleri anlamak i\u00e7in \u00e7ok kullan\u0131\u015fl\u0131d\u0131r.\n\n<h3>Veri Ya\u015fam D\u00f6ng\u00fcs\u00fc Y\u00f6netimi (Data Lifecycle Management)<\/h3>\n\nZamanla, eski model \u00e7\u0131kt\u0131lar\u0131 art\u0131k aktif olarak kullan\u0131lmayabilir ancak yine de saklanmas\u0131 gerekebilir. MongoDB'nin TTL (Time-To-Live) indeksleri, belirli bir s\u00fcre sonra dok\u00fcmanlar\u0131 otomatik olarak silerek veri saklama maliyetlerini d\u00fc\u015f\u00fcrmenize yard\u0131mc\u0131 olur.\n\n\u00d6rne\u011fin, 2 y\u0131l sonra otomatik olarak silinmesini istedi\u011finiz tahminler i\u00e7in:<\/code><\/pre>\n
python
\n# ‘timestamp’ alan\u0131na 2 y\u0131l (730 g\u00fcn) sonra silinecek bir TTL indeks ekleme
\n# 60 * 60 * 24 * 730 saniyedir.
\ncollection.create_index([(“timestamp”, 1)], expireAfterSeconds=60*60*24*730)<\/p>\n
Bu \u00f6zellik, \u00f6zellikle s\u00fcrekli veri \u00fcreten sistemlerde depolama alan\u0131n\u0131 y\u00f6netmek i\u00e7in \u00f6nemlidir.\n\n<h3>Sharding ile \u00d6l\u00e7eklenebilirlik<\/h3>\n\n\u00c7ok b\u00fcy\u00fck veri k\u00fcmeleriyle \u00e7al\u0131\u015f\u0131rken, MongoDB'nin "sharding" \u00f6zelli\u011fi devreye girer. Sharding, verileri birden fazla sunucuya (shard) da\u011f\u0131tarak hem okuma hem de yazma performans\u0131n\u0131 art\u0131r\u0131r ve sistemin yatay olarak \u00f6l\u00e7eklenmesini sa\u011flar. Model \u00e7\u0131kt\u0131lar\u0131 gibi s\u00fcrekli b\u00fcy\u00fcyen veri k\u00fcmeleri i\u00e7in sharding, y\u00fcksek kullan\u0131labilirlik ve performans elde etmenin anahtar\u0131d\u0131r. Hangi alan\u0131n shard anahtar\u0131 olaca\u011f\u0131n\u0131 dikkatlice se\u00e7mek, veri da\u011f\u0131l\u0131m\u0131n\u0131 optimize etmek i\u00e7in \u00f6nemlidir. Genellikle zaman damgalar\u0131 veya s\u0131k\u00e7a sorgulanan ve y\u00fcksek kardinaliteye sahip alanlar iyi shard anahtarlar\u0131 olabilir.\n\n<h2>Sonu\u00e7<\/h2>\n\nYapay zeka ve makine \u00f6\u011frenmesi modellerinin \u00fcretti\u011fi \u00e7\u0131kt\u0131lar, g\u00fcn\u00fcm\u00fcz\u00fcn veri odakl\u0131 d\u00fcnyas\u0131nda giderek daha de\u011ferli hale geliyor. Bu \u00e7\u0131kt\u0131lar\u0131 etkili bir \u015fekilde depolamak, y\u00f6netmek ve sorgulamak, modellerin ger\u00e7ek potansiyelini ortaya \u00e7\u0131karman\u0131n ve i\u015f kararlar\u0131n\u0131 desteklemenin temelini olu\u015fturur. MongoDB'nin esnek \u015femas\u0131, g\u00fc\u00e7l\u00fc sorgu yetenekleri, \u00f6l\u00e7eklenebilirli\u011fi ve zengin ekosistemi, model sonras\u0131 verileriniz i\u00e7in ideal bir veritaban\u0131 \u00e7\u00f6z\u00fcm\u00fc sunar.\n\n\u0130ster bir ki\u015fiselle\u015ftirme motoru geli\u015ftiriyor olun, ister siber g\u00fcvenlik tehditlerini izliyor olun, MongoDB ile verilerinizi y\u00f6netmek, operasyonel verimlili\u011finizi art\u0131rman\u0131za, daha derin i\u00e7g\u00f6r\u00fcler elde etmenize ve nihayetinde daha ak\u0131ll\u0131 kararlar alman\u0131za yard\u0131mc\u0131 olacakt\u0131r. Bu makalede ele ald\u0131\u011f\u0131m\u0131z temel kavramlar, ad\u0131m ad\u0131m \u00f6rnekler ve ger\u00e7ek d\u00fcnya senaryolar\u0131, MongoDB'yi model sonras\u0131 verilerinizi y\u00f6netmek i\u00e7in nas\u0131l kullanabilece\u011finiz konusunda size sa\u011flam bir ba\u015flang\u0131\u00e7 noktas\u0131 sunmu\u015ftur.\n\n<h3>S\u0131k\u00e7a Sorulan Sorular (SSS)<\/h3>\n\n<ul>\n    <li><strong>MongoDB'nin ili\u015fkisel veritabanlar\u0131na g\u00f6re avantajlar\u0131 nelerdir?<\/strong>\n        <p>MongoDB'nin en b\u00fcy\u00fck avantaj\u0131 \u015fema esnekli\u011fidir. Yap\u0131s\u0131 s\u00fcrekli de\u011fi\u015febilen veya karma\u015f\u0131k, i\u00e7 i\u00e7e ge\u00e7mi\u015f verilere sahip yapay zeka \u00e7\u0131kt\u0131lar\u0131 i\u00e7in ili\u015fkisel veritabanlar\u0131ndaki sabit \u015femalara g\u00f6re daha kolay uyum sa\u011flar. Ayr\u0131ca, yatay \u00f6l\u00e7eklenebilirlik (sharding) ile b\u00fcy\u00fck veri hacimlerini daha verimli y\u00f6netebilir.<\/p>\n    <\/li>\n    <li><strong>Model \u00e7\u0131kt\u0131lar\u0131n\u0131 saklarken en s\u0131k kar\u015f\u0131la\u015f\u0131lan zorluklar nelerdir ve MongoDB bu zorluklar\u0131 nas\u0131l a\u015far?<\/strong>\n        <p>En s\u0131k kar\u015f\u0131la\u015f\u0131lan zorluklar veri hacminin b\u00fcy\u00fckl\u00fc\u011f\u00fc, veri yap\u0131s\u0131n\u0131n de\u011fi\u015fkenli\u011fi ve h\u0131zl\u0131 sorgulama ihtiyac\u0131d\u0131r. MongoDB, esnek dok\u00fcman yap\u0131s\u0131yla de\u011fi\u015fken veri yap\u0131lar\u0131n\u0131 kolayca y\u00f6netir, indeksleme ve agregasyon \u00e7er\u00e7evesi ile h\u0131zl\u0131 sorgulamay\u0131 m\u00fcmk\u00fcn k\u0131lar ve sharding ile b\u00fcy\u00fck veri hacimlerini \u00f6l\u00e7eklenebilir bir \u015fekilde y\u00f6netir.<\/p>\n    <\/li>\n    <li><strong>Hangi t\u00fcr makine \u00f6\u011frenmesi modellerinin \u00e7\u0131kt\u0131lar\u0131 MongoDB'de en iyi \u015fekilde saklan\u0131r?<\/strong>\n        <p>Temel olarak her t\u00fcrl\u00fc modelin \u00e7\u0131kt\u0131s\u0131 saklanabilir. Ancak, \u00f6zellikle tahminlerin olas\u0131l\u0131klar\u0131, s\u0131n\u0131fland\u0131rma etiketleri, \u00f6neri listeleri, nesne tespit bounding box'lar\u0131, metin analizi sonu\u00e7lar\u0131 gibi yap\u0131land\u0131r\u0131lm\u0131\u015f veya yar\u0131 yap\u0131land\u0131r\u0131lm\u0131\u015f \u00e7\u0131kt\u0131lar MongoDB'nin dok\u00fcman yap\u0131s\u0131na \u00e7ok iyi uyum sa\u011flar.<\/p>\n    <\/li>\n    <li><strong>MongoDB'de veri g\u00fcvenli\u011fi nas\u0131l sa\u011flan\u0131r?<\/strong>\n        <p>MongoDB, rol tabanl\u0131 eri\u015fim kontrol\u00fc (RBAC), \u015fifreleme (hem aktar\u0131m s\u0131ras\u0131nda hem de dura\u011fan veri i\u00e7in), a\u011f g\u00fcvenli\u011fi ve denetim g\u00fcnl\u00fckleri gibi \u00e7e\u015fitli g\u00fcvenlik \u00f6zellikleri sunar. Bu \u00f6zellikler, hassas model \u00e7\u0131kt\u0131lar\u0131n\u0131z\u0131n g\u00fcvenli\u011fini sa\u011flamak i\u00e7in yap\u0131land\u0131r\u0131labilir.<\/p>\n    <\/li>\n    <li><strong>MongoDB'de sorgu performans\u0131n\u0131 optimize etmek i\u00e7in nelere dikkat etmeliyim?<\/strong>\n        <p>Do\u011fru alanlara etkili indeksler olu\u015fturmak en \u00f6nemli ad\u0131md\u0131r. S\u0131k\u00e7a sorgulanan alanlar\u0131, s\u0131ralama kriterlerini ve filtreleme ko\u015fullar\u0131n\u0131 g\u00f6z \u00f6n\u00fcnde bulundurarak tek alanl\u0131 veya bile\u015fik indeksler olu\u015fturun. Ayr\u0131ca, sorgu planlar\u0131n\u0131 analiz ederek ve agregasyon \u00e7er\u00e7evesini verimli kullanarak performans\u0131 optimize edebilirsiniz.<\/p>\n    <\/li>\n<\/ul><\/code><\/pre>\n","protected":false},"excerpt":{"rendered":"<title>MongoDB ile Model Sonras\u0131 Veri Saklama ve Sorgulama<\/title> <meta name="description" content="Makine \u00f6\u011frenmesi modellerinizin \u00e7\u0131kt\u0131s\u0131n\u0131 MongoDB’de verimli bir \u015fekilde…","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":[676],"tags":[],"class_list":{"0":"post-41457","1":"post","2":"type-post","3":"status-publish","4":"format-standard","6":"category-mongodb","7":"cs-entry","8":"cs-video-wrap"},"yoast_head":"\nMongoDB ile Model Sonras\u0131 Veri Saklama ve Sorgulama: 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