Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Penyajian fitur
Dokumen ini menjelaskan opsi Anda untuk menyediakan
fitur untuk pelatihan dan inferensi
model BigQuery ML. Untuk semua opsi, Anda harus menyimpan fitur di
tabel BigQuery sebagai langkah pertama prasyarat.
Keakuratan point-in-time
Data yang digunakan untuk melatih model sering kali memiliki dependensi waktu yang terintegrasi. Saat
membuat tabel fitur untuk fitur yang sensitif terhadap waktu, sertakan kolom stempel waktu
untuk mewakili nilai fitur seperti yang ada pada waktu tertentu untuk setiap
baris. Kemudian, Anda dapat menggunakan fungsi pencarian titik waktu saat membuat kueri data dari
tabel fitur ini untuk memastikan tidak ada kebocoran
data antara
pelatihan dan penayangan. Proses ini memungkinkan ketepatan point-in-time.
Gunakan fungsi berikut untuk menentukan batas waktu tertentu saat mengambil fitur yang sensitif terhadap waktu:
Untuk melatih model dan melakukan inferensi batch di BigQuery ML, Anda
dapat mengambil fitur menggunakan salah satu fungsi pencarian titik waktu yang dijelaskan
di bagian Keakuratan titik waktu. Anda dapat menyertakan fungsi ini dalam klausa query_statement dari pernyataan CREATE MODEL untuk pelatihan, atau dalam klausa query_statement dari fungsi nilai tabel yang sesuai, seperti ML.PREDICT, untuk penayangan.
Menayangkan fitur dengan Vertex AI Feature Store
Untuk menayangkan fitur ke model BigQuery ML yang
terdaftar di Vertex AI,
Anda dapat menggunakan
Vertex AI Feature Store.
Vertex AI Feature Store berfungsi di atas tabel fitur di BigQuery untuk mengelola dan menyalurkan fitur dengan latensi rendah. Anda dapat
menggunakan penyaluran online
untuk mengambil fitur secara real time untuk prediksi online, dan Anda dapat menggunakan
penyaluran offline
untuk mengambil fitur untuk pelatihan model.
Untuk informasi selengkapnya tentang cara menyiapkan data fitur BigQuery
yang akan digunakan di Vertex AI Feature Store, lihat
Menyiapkan sumber data.
[[["Mudah dipahami","easyToUnderstand","thumb-up"],["Memecahkan masalah saya","solvedMyProblem","thumb-up"],["Lainnya","otherUp","thumb-up"]],[["Sulit dipahami","hardToUnderstand","thumb-down"],["Informasi atau kode contoh salah","incorrectInformationOrSampleCode","thumb-down"],["Informasi/contoh yang saya butuhkan tidak ada","missingTheInformationSamplesINeed","thumb-down"],["Masalah terjemahan","translationIssue","thumb-down"],["Lainnya","otherDown","thumb-down"]],["Terakhir diperbarui pada 2025-08-17 UTC."],[[["\u003cp\u003eFeatures must be saved in BigQuery tables before they can be used for BigQuery ML model training and inference.\u003c/p\u003e\n"],["\u003cp\u003eIncluding a timestamp column in feature tables allows for point-in-time correctness, preventing data leakage between training and serving.\u003c/p\u003e\n"],["\u003cp\u003e\u003ccode\u003eML.FEATURES_AT_TIME\u003c/code\u003e and \u003ccode\u003eML.ENTITY_FEATURES_AT_TIME\u003c/code\u003e functions are used to specify point-in-time cutoffs when retrieving time-sensitive features.\u003c/p\u003e\n"],["\u003cp\u003eBigQuery ML models can use point-in-time lookup functions in \u003ccode\u003eCREATE MODEL\u003c/code\u003e statements or in table-valued functions like \u003ccode\u003eML.PREDICT\u003c/code\u003e to retrieve features for training and batch inference.\u003c/p\u003e\n"],["\u003cp\u003eVertex AI Feature Store can be used to manage and serve features with low latency for BigQuery ML models registered in Vertex AI, supporting both real-time online prediction and offline model training.\u003c/p\u003e\n"]]],[],null,["# Feature serving\n===============\n\nThis document describes your options for making\n[features](/bigquery/docs/preprocess-overview) available for BigQuery ML\nmodel training and inference. For all options, you must save the features in\nBigQuery tables as a prerequisite first step.\n\nPoint-in-time correctness\n-------------------------\n\nThe data used to train a model often has time dependencies built into it. When\nyou create a feature table for time sensitive features, include a timestamp\ncolumn to represent the feature values as they existed at a given time for each\nrow. You can then use point-in-time lookup functions when querying data from\nthese feature tables in order to ensure that there is no [data\nleakage](https://en.wikipedia.org/wiki/Leakage_(machine_learning)) between\ntraining and serving. This process enables point-in-time correctness.\n\nUse the following functions to specify point-in-time cutoffs when retrieving\ntime sensitive features:\n\n- [`ML.FEATURES_AT_TIME`](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-feature-time)\n- [`ML.ENTITY_FEATURES_AT_TIME`](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-entity-feature-time)\n\nServe features in BigQuery ML\n-----------------------------\n\nTo train models and perform batch inference in BigQuery ML, you\ncan retrieve features using one of the point-in-time lookup functions described\nin the [Point-in-time correctness](#point-in-time_correctness) section. You can\ninclude these functions in the\n[`query_statement` clause](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-create#query_statement) of the `CREATE MODEL` statement for\ntraining, or in the `query_statement` clause of the appropriate table-valued\nfunction, such as\n[`ML.PREDICT`](/bigquery/docs/reference/standard-sql/bigqueryml-syntax-predict),\nfor serving.\n\nServe features with Vertex AI Feature Store\n-------------------------------------------\n\nTo serve features to BigQuery ML models that are\n[registered in Vertex AI](/bigquery/docs/managing-models-vertex#register_models),\nyou can use\n[Vertex AI Feature Store](/vertex-ai/docs/featurestore/latest/overview).\nVertex AI Feature Store works on top of feature tables in\nBigQuery to manage and serve features with low latency. You can\nuse [online serving](/vertex-ai/docs/featurestore/latest/serve-feature-values)\nto retrieve features in real time for online prediction, and you can use\n[offline serving](/vertex-ai/docs/featurestore/latest/serve-historical-features)\nto retrieve features for model training.\n\nFor more information about preparing BigQuery feature data\nto be used in Vertex AI Feature Store, see\n[Prepare data source](/vertex-ai/docs/featurestore/latest/prepare-data-source)."]]
