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Halaman ini menjelaskan antarmuka yang dapat Anda gunakan untuk berinteraksi dengan Vertex AI dan kapan Anda sebaiknya menggunakannya. Anda dapat menggunakan antarmuka ini bersama salah satu solusi notebook Vertex AI.
Beberapa operasi Vertex AI hanya tersedia melalui antarmuka tertentu, sehingga Anda mungkin perlu beralih antar-antarmuka selama alur kerja Anda.
Misalnya, di Vertex AI Experiments, Anda harus menggunakan API untuk mencatat data ke operasi eksperimen, tetapi Anda dapat melihat hasilnya di Konsol Google Cloud.
Konsol
Konsol adalah antarmuka pengguna grafis yang dapat Anda gunakan untuk menangani resource machine learning. Google Cloud
Di Google Cloud konsol, Anda dapat mengelola set data terkelola, model, endpoint, dan tugas. Anda juga dapat mengakses layanan Google Cloud lainnya, seperti Cloud Storage dan BigQuery, melalui konsol.
Gunakan konsol Google Cloud jika Anda lebih suka melihat dan mengelola resource dan visualisasi Vertex AI melalui antarmuka pengguna grafis.
Untuk mengetahui informasi selengkapnya, lihat halaman Dashbord di bagian Vertex AI:
Terraform adalah alat infrastruktur sebagai kode (IaC) yang dapat Anda gunakan untuk menyediakan infrastruktur, seperti resource dan izin, bagi beberapa layanan, termasuk Vertex AI.Google Cloud
Anda dapat menentukan resource dan izin Vertex AI untuk project Google Cloud
Anda dalam file konfigurasi Terraform. Selanjutnya, Anda dapat menggunakan Terraform untuk menerapkan konfigurasi ke project dengan membuat resource baru dan memperbarui resource yang ada.
Gunakan Terraform jika Anda ingin menstandarkan infrastruktur untuk resource Vertex AI di project Google Cloud Anda dan memperbarui infrastruktur project Google Cloudyang sudah ada, sekaligus memenuhi dependensi resource.
SDK Vertex AI untuk Python mirip dengan library klien Python Vertex AI, hanya saja SDK ini lebih umum dan kurang terperinci. Untuk informasi selengkapnya, lihat Memahami perbedaan antara SDK dan library klien.
Library klien menggunakan konvensi natural dari setiap bahasa yang didukung untuk memanggil API Vertex AI dan mengurangi kode boilerplate yang harus Anda tulis.
Vertex AI REST API menyediakan layanan RESTful untuk mengelola tugas, model, dan endpoint, serta untuk membuat inferensi dengan model yang dihosting di Google Cloud.
Gunakan REST API jika Anda perlu menggunakan library Anda sendiri untuk memanggil API Vertex AI dari aplikasi Anda.
[[["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 2025-08-19 UTC."],[],[],null,["# Interfaces for Vertex AI\n\nThis page describes the interfaces that you can use to interact with\nVertex AI and when you should use them. You can use these interfaces\nalong with one of Vertex AI's\n[notebook solutions](/vertex-ai/docs/workbench/notebook-solution).\n\nSome Vertex AI operations are only available through specific\ninterfaces, so you may need to switch between interfaces during your workflow.\nFor example, in Vertex AI Experiments, you must use the API to log data\nto an experiment run, but you can view the results in the console. \n\n### Console\n\nThe Google Cloud console is a graphical user interface that you can use to\nwork with your machine learning resources.\n\nIn the Google Cloud console, you can manage your ,\nmodels, endpoints, and jobs. You can also access other Google Cloud services,\nsuch as Cloud Storage and BigQuery, through the console.\n\nUse the Google Cloud console if you prefer to view and manage your\nVertex AI resources and visualizations through a graphical user\ninterface.\n\nFor more information, see the **Dashboard** page of the Vertex AI section:\n\n[Go to the Dashboard](https://console.cloud.google.com/vertex-ai/)\n\n### gcloud\n\nThe [Google Cloud command-line interface (CLI)](/sdk/gcloud) is a set of tools for\ncreating and managing Google Cloud resources using the `gcloud` command.\n\nUse the Google Cloud CLI when you want to manage your Vertex AI\nresources from the command line or through scripts and other automation.\n\nFor more information, see [Install the gcloud CLI](/sdk/docs/install) and the\n[`gcloud ai`](/sdk/gcloud/reference/ai) reference.\n\n### Terraform\n\nTerraform is an (IaC) tool that you can use to\nprovision the infrastructure, such as resources and permissions, for multiple\nGoogle Cloud services, including Vertex AI.\n\nYou can define the Vertex AI resources and permissions for your Google Cloud\nproject in a Terraform configuration file. You can then use Terraform to apply\nthe configuration to your project by creating new resources and updating\nexisting resources.\n\nUse Terraform if you want to standardize the infrastructure for Vertex AI\nresources in your Google Cloud project and update the existing Google Cloud\nproject infrastructure while fulfilling resource dependencies.\n\nTo get started, see [Terraform support for Vertex AI](/vertex-ai/docs/start/use-terraform-vertex-ai).\n\n### Python\n\nUse the [Vertex AI SDK for Python](/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk) to programmatically automate your\nVertex AI workflow.\n\nThe Vertex AI SDK for Python is similar to the Vertex AI Python client\nlibrary, except the SDK is higher-level and less granular. For more\ninformation, see the [Understand the SDK and client library\ndifferences](/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk#sdk-vs-client-library).\n\nTo get started, see [Install the Vertex AI SDK](/vertex-ai/docs/start/install-sdk).\n\n### Client libraries\n\nClient libraries use each supported language's natural conventions to call the\nVertex AI API and reduce boilerplate code that you have to write.\n\nThe following languages are supported for Vertex AI:\n\n- Python. The Vertex AI Python client library is installed when you\n install the [Vertex AI SDK for Python](/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk).\n\n- Java\n\n- Node.js\n\n- C#\n\n- Go\n\nFor more information, see [Install the Vertex AI client libraries](/vertex-ai/docs/start/client-libraries).\n\n### REST\n\nThe Vertex AI REST API provides RESTful services for managing jobs,\nmodels, and endpoints, and for making inferences with hosted models\non Google Cloud.\n\nUse the REST API if you need to use your own libraries to call the\nVertex AI API from your application.\n\nTo get started, see the [Vertex AI API REST reference](/vertex-ai/docs/reference/rest).\n\nWhat's next\n-----------\n\n- [Set up a project and a development environment](/vertex-ai/docs/start/cloud-environment).\n- [Choose a training method](/vertex-ai/docs/start/training-methods).\n- Tutorials for [Image](/vertex-ai/docs/tutorials/image-classification-automl/overview), [Tabular](/vertex-ai/docs/tutorials/tabular-automl/overview), and [Custom training](/vertex-ai/docs/tutorials/image-classification-custom/overview).\n- Learn [best practices for implementing custom-trained ML models on\n Vertex AI](/architecture/ml-on-gcp-best-practices)."]]
