Vertex AI は、Google Cloud で ML モデルを構築してデプロイするための統合プラットフォームです。カスタム モデルと AutoML モデルの両方をサポートし、Vertex Pipelines、Vertex Vizier、Vertex Explainable AI、Vertex Feature Store など、ML 開発用のさまざまなツールとサービスを提供します。Vertex AI を使用すると、チケットの優先度と解決時間を予測するモデルを作成できます。これらは、カスタム トレーニング データと評価指標を必要とするドメイン固有のタスクです。Cloud Natural Language API は、感情分析、エンティティ分析、構文分析、コンテンツ分類などの自然言語理解機能を提供する、トレーニング済みのサービスです。Cloud Natural Language API を使用すると、サポート チケットの感情分析を実行できます。これは、ドメイン固有の知識や専門用語を必要としない一般的なタスクです。その他のオプションは、指定されたアーキテクチャには適していません。AutoML Natural Language と AutoML Vision は、ユーザーが独自のデータとラベルを使用してカスタムの自然言語モデルとビジョン モデルを作成できるサービスです。感情分析には必要ありません。Cloud Natural Language API はすでにこの機能を提供しているためです。 Cloud Vision API は、オブジェクト検出、顔検出、テキスト検出、画像ラベル付けなどの画像分析機能を提供する事前トレーニング済みのサービスです。サポート チケットには画像が含まれていないことが想定されているため、この API はサポート チケットには関係ありません。参考資料:
* Vertex AI ドキュメント
* Cloud Natural Language API ドキュメント

The best option for deploying a new version of a model to a production Vertex AI endpoint that is serving traffic, directing all user traffic to the new model, and deploying the model with minimal disruption to your application, is to create a new model, set the parentModel parameter to the model ID of the currently deployed model, upload the model to Vertex AI Model Registry, deploy the new model to the existing endpoint, and set the new model to 100% of the traffic. This option allows you to leverage the power and simplicity of Vertex AI to update your model version and serve online predictions with low latency. Vertex AI is a unified platform for building and deploying machine learning solutions on Google Cloud. Vertex AI can deploy a trained model to an online prediction endpoint, which can provide low-latency predictions for individual instances. A model is a resource that represents a machine learning model that you can use for prediction. A model can have one or more versions, which are different implementations of the same model. A model version can have different parameters, code, or data than another version of the same model. A model version can help you experiment and iterate on your model, and improve the model performance and accuracy. A parentModel parameter is a parameter that specifies the model ID of the model that the new model version is based on. A parentModel parameter can help you inherit the settings and metadata of the existing model, and avoid duplicating the model configuration. Vertex AI Model Registry is a service that can store and manage your machine learning models on Google Cloud. Vertex AI Model Registry can help you upload and organize your models, and track the model versions and metadata. An endpoint is a resource that provides the service endpoint (URL) you use to request the prediction. An endpoint can have one or more deployed models, which are instances of model versions that are associated with physical resources. A deployed model can help you serve online predictions with low latency, and scale up or down based on the traffic. By creating a new model, setting the parentModel parameter to the model ID of the currently deployed model, uploading the model to Vertex AI Model Registry, deploying the new model to the existing endpoint, and setting the new model to 100% of the traffic, you can deploy a new version of a model to a production Vertex AI endpoint that is serving traffic, direct all user traffic to the new model, and deploy the model with minimal disruption to your application1.
The other options are not as good as option C, for the following reasons:
* Option A: Creating a new endpoint, creating a new model, setting it as the default version, uploading the model to Vertex AI Model Registry, deploying the new model to the new endpoint, and updating Cloud DNS to point to the new endpoint would require more skills and steps than creating a new model, setting the parentModel parameter to the model ID of the currently deployed model, uploading the model to Vertex AI Model Registry, deploying the new model to the existing endpoint, and setting the new model to 100% of the traffic. Cloud DNS is a service that can provide reliable and scalable Domain Name System (DNS) services on Google Cloud. Cloud DNS can help you manage your DNS records, and resolve domain names to IP addresses. By updating Cloud DNS to point to the new endpoint, you can redirect the user traffic to the new endpoint, and avoid breaking the existing application. However, creating a new endpoint, creating a new model, setting it as the default version, uploading the model to Vertex AI Model Registry, deploying the new model to the new endpoint, and updating Cloud DNS to point to the new endpoint would require more skills and steps than creating a new model, setting the parentModel parameter to the model ID of the currently deployed model, uploading the model to Vertex AI Model Registry, deploying the new model to the existing endpoint, and setting the new model to
100% of the traffic. You would need to write code, create and configure the new endpoint, create and configure the new model, upload the model to Vertex AI Model Registry, deploy the model to the new endpoint, and update Cloud DNS to point to the new endpoint. Moreover, this option would create a new endpoint, which can increase the maintenance and management costs2.
* Option B: Creating a new endpoint, creating a new model, setting the parentModel parameter to the model ID of the currently deployed model and setting it as the default version, uploading the model to Vertex AI Model Registry, and deploying the new model to the new endpoint and setting the new model to 100% of the traffic would require more skills and steps than creating a new model, setting the parentModel parameter to the model ID of the currently deployed model, uploading the model to Vertex AI Model Registry, deploying the new model to the existing endpoint, and setting the new model to
100% of the traffic. A parentModel parameter is a parameter that specifies the model ID of the model that the new model version is based on. A parentModel parameter can help you inherit the settings and metadata of the existing model, and avoid duplicating the model configuration. A default version is a model version that is used for prediction when no other version is specified. A default version can help you simplify the prediction request, and avoid specifying the model version every time. By setting the parentModel parameter to the model ID of the currently deployed model and setting it as the default version, you can create a new model that is based on the existing model, and use it for prediction without specifying the model version. However, creating a new endpoint, creating a new model, setting the parentModel parameter to the model ID of the currently deployed model and setting it as the default version, uploading the model to Vertex AI Model Registry, and deploying the new model to the new endpoint and setting the new model to 100% of the traffic would require more skills and steps than creating a new model, setting the parentModel parameter to the model ID of the currently deployed
* model, uploading the model to Vertex AI Model Registry, deploying the new model to the existing endpoint, and setting the new model to 100% of the traffic. You would need to write code, create and configure the new endpoint, create and configure the new model, upload the model to Vertex AI Model Registry, and deploy the model to the new endpoint. Moreover, this option would create a new endpoint, which can increase the maintenance and management costs2.
* Option D: Creating a new model, setting it as the default version, uploading the model to Vertex AI Model Registry, and deploying the new model to the existing endpoint would not allow you to inherit the settings and metadata of the existing model, and could cause errors or poor performance. A default version is a model version that is used for prediction when no other version is specified. A default version can help you simplify the prediction request, and avoid specifying the model version every time.
新しいモデルをデフォルト バージョンとして設定すると、モデル バージョンを指定せずに新しいモデルを予測に使用できます。ただし、新しいモデルを作成し、それをデフォルト バージョンとして設定し、そのモデルを Vertex AI Model Registry にアップロードして、新しいモデルを既存のエンドポイントにデプロイすると、既存のモデルの設定とメタデータを継承できず、エラーが発生したり、パフォーマンスが低下したりする可能性があります。コードを記述し、新しいモデルを作成して構成し、そのモデルを Vertex AI Model Registry にアップロードして、そのモデルを既存のエンドポイントにデプロイする必要があります。さらに、このオプションでは、parentModel パラメータが現在デプロイされているモデルのモデル ID に設定されないため、既存のモデルの設定とメタデータを継承できず、モデル バージョン間で不整合や競合が発生する可能性があります2。
参考文献:
* Google Cloud 認定資格の準備: 機械学習エンジニア、コース 3: 本番環境 ML システム、第 2 週: ML 予測の提供
* Google Cloud Professional Machine Learning Engineer 試験ガイド、セクション 3: 本番環境での ML モデルのスケーリング、3.1 本番環境への ML モデルのデプロイ
* 公式 Google Cloud Certified Professional Machine Learning Engineer 学習ガイド、第 6 章:
プロダクション ML システム、セクション 6.2: ML 予測の提供
* 頂点AI
* クラウドDNS

説明