Tetap teratur dengan koleksi
Simpan dan kategorikan konten berdasarkan preferensi Anda.
Penyiapan mencakup informasi tentang penyiapan project untuk
Vertex AI Feature Store (Lama) dan izin yang diperlukan untuk menggunakan
Vertex AI Feature Store (Lama).
Konfigurasikan project
Prosedur berikut menjelaskan cara membuat project baru dan mengaktifkan
Vertex AI API. API ini diperlukan untuk menggunakan
Vertex AI Feature Store (Lama). Jika sudah memiliki project dengan
Vertex AI API yang diaktifkan, Anda dapat menggunakan project tersebut, bukan
membuat project baru.
Sign in to your Google Cloud account. If you're new to
Google Cloud,
create an account to evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
Selain izin pengguna, Vertex AI Feature Store (Lama) bertindak atas nama
Anda untuk melakukan operasi seperti mengakses data sumber. Untuk melakukannya,
Vertex AI Feature Store (Lama) menggunakan agen layanan:
service-PROJECT_NUMBER@gcp-sa-aiplatform..
Secara default, agen layanan memberi Vertex AI Feature Store (Lama) akses
ke data sumber di project yang sama tempat featurestore Anda berada. Jika
data sumber berada dalam project yang berbeda dengan featurestore, Anda harus memberikan
izin kepada agen layanan untuk mengakses project tempat data sumber
berada.
Admin Vertex AI memiliki hak istimewa administrator Vertex AI Feature Store (Lama). Jika Anda memerlukan lebih banyak perincian, Vertex AI Feature Store (Lama)
menyediakan kumpulan peran IAM yang telah ditetapkan. Peran ini memberikan
kumpulan izin yang berbeda berdasarkan persona berikut:
Operasi IT dan DevOps
Operasi IT dan DevOps mengelola Google Cloud resource serta bertanggung jawab untuk
membuat featurestore dan menyesuaikan performanya. Anda dapat menggunakan peran
featurestoreAdmin atau featurestoreInstanceCreator. Dengan peran pembuat instance, Anda dapat mengelola featurestore, tetapi mencegah Anda melihat data atau menulis data ke featurestore.
Data scientist dan data engineer
Data scientist dan data engineer membuat fitur dan menulis data ke
featurestore. Anda dapat menggunakan peran featurestoreResourceEditor untuk
mengelola jenis dan fitur entity, serta menggunakan peran featurestoreDataWriter untuk
membaca dan menulis nilai fitur.
Peneliti ML dan analis bisnis
Peneliti dan analis bisnis ML mencari fitur dan nilai ekspor untuk melatih model atau membuat prediksi; mereka tidak perlu membuat fitur
baru atau menulis data. Anda dapat menggunakan peran featurestoreResourceViewer untuk menelusuri
atau menjelajahi fitur, dan peran featurestoreDataViewer untuk membaca nilai
fitur.
Vertex AI Feature Store (Lama) menerapkan kuota dan batas untuk membantu Anda mengelola
resource dengan menetapkan batas penggunaan Anda sendiri dan melindungi komunitas
pengguna Google Cloud dengan mencegah lonjakan penggunaan yang tidak terduga. Untuk mencegah Anda mencapai
batasan tak terencana, tinjau kuota Vertex AI Feature Store (Legacy)
di halaman Kuota dan batas. Misalnya,
Vertex AI Feature Store (Legacy) menetapkan kuota jumlah node penyaluran
online dan kuota pada jumlah permintaan penyaluran online yang dapat Anda buat per
menit.
[[["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 2025-09-02 UTC."],[],[],null,["# Setup includes information about setting up a project for\nVertex AI Feature Store (Legacy) and the required permissions for using\nVertex AI Feature Store (Legacy).\n\nConfigure project\n-----------------\n\nThe following procedure describes how to create a new project and enable the\nVertex AI API. This API is required to use\nVertex AI Feature Store (Legacy). If you already have an existing project with\nthe Vertex AI API enabled, you can use that project instead of\ncreating a new project.\n\n- Sign in to your Google Cloud account. If you're new to Google Cloud, [create an account](https://console.cloud.google.com/freetrial) to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vertex AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)\n\n- In the Google Cloud console, on the project selector page,\n select or create a Google Cloud project.\n\n [Go to project selector](https://console.cloud.google.com/projectselector2/home/dashboard)\n-\n [Verify that billing is enabled for your Google Cloud project](/billing/docs/how-to/verify-billing-enabled#confirm_billing_is_enabled_on_a_project).\n\n-\n\n\n Enable the Vertex AI API.\n\n\n [Enable the API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com)\n\nVertex AI Feature Store (Legacy) service agent\n----------------------------------------------\n\nIn addition to user permissions, Vertex AI Feature Store (Legacy) acts on your\nbehalf to perform operations such as accessing source data. To do so,\nVertex AI Feature Store (Legacy) uses a service agent:\n`service-`\u003cvar class=\"readonly\" translate=\"no\"\u003ePROJECT_NUMBER\u003c/var\u003e`@gcp-sa-aiplatform.iam.gserviceaccount.com`.\nBy default, the service agent grants Vertex AI Feature Store (Legacy) access\nto source data in the same project where your featurestore is located. If the\nsource data is in a different project from your featurestore, you must grant the\nservice agent permission to access the project where the source data is\nlocated.\n\nFor more information, see [Grant Vertex AI service agents access to other\nresources](/vertex-ai/docs/general/access-control#grant_service_agents_access_to_other_resources).\n\nIAM permissions\n---------------\n\nVertex AI admins have Vertex AI Feature Store (Legacy) administrator\nprivileges. If you require more granularity, Vertex AI Feature Store (Legacy)\nprovides a set of predefined IAM roles. These roles provide\ndifferent sets of permissions that are based on the following personas:\n\nIT operations and DevOps\n: IT operations and DevOps manage Google Cloud resources and are responsible for\n creating featurestores and tuning their performance. You can use the\n `featurestoreAdmin` or `featurestoreInstanceCreator` role. The instance creator\n role lets you manage featurestores but prevents you from viewing data or\n writing data to the featurestores.\n\nData scientists and data engineers\n: Data scientists and data engineers create features and write data to\n featurestores. You can use the `featurestoreResourceEditor` role to\n manage entity types and features, and use the `featurestoreDataWriter` role to\n read and write feature values.\n\nML researchers and business analysts\n: ML researchers and business analysts search for features and export values for\n training models or making predictions; they don't need to create new features or\n write data. You can use the `featurestoreResourceViewer` role to search\n or browse for features and the `featurestoreDataViewer` role to read feature\n values.\n\nFor descriptions of each role and their associated permissions, see\n[Predefined roles for\nVertex AI](/vertex-ai/docs/general/access-control#predefined-roles).\n\nQuotas and limits\n-----------------\n\nVertex AI Feature Store (Legacy) enforces quotas and limits to help you manage\nresources by setting your own usage limits and to protect the community of\nGoogle Cloud users by preventing unforeseen spikes in usage. To prevent you from\nhitting unplanned constraints, review Vertex AI Feature Store (Legacy) quotas\non the [Quotas and limits](/vertex-ai/quotas#featurestore) page. For example,\nVertex AI Feature Store (Legacy) sets a quota on the number of online serving\nnodes and a quota on the number of online serving requests that you can make per\nminute.\n\nWhat's next\n-----------\n\n- Learn about [Manage featurestores](/vertex-ai/docs/featurestore/managing-featurestores).\n- Learn about [best practices](/vertex-ai/docs/featurestore/best-practices) for using Vertex AI Feature Store (Legacy)."]]
