このコースでは、Google Cloud で最先端の ML パイプラインに携わっている ML エンジニアおよびトレーナーたちから知識を吸収することができます。 最初のいくつかのモジュールで、ML パイプラインとメタデータの管理用 TensorFlow を基盤とする Google の本番環境向け機械学習プラットフォーム TensorFlow Extended（TFX）について説明します。パイプラインのコンポーネントについて、そして TFX を使用したパイプラインのオーケストレーションについて学習します。また、継続的インテグレーションと継続的デプロイを通じたパイプラインの自動化の方法と、ML メタデータの管理方法についても学習します。その後、焦点を変えて、TensorFlow、PyTorch、Scikit Learn、XGBoost などの複数の ML フレームワーク全体にわたる ML パイプラインの自動化と再利用の方法について説明します。 さらに、Google Cloud のもう 1 つのツール、Cloud Composer を継続的なトレーニング パイプラインのオーケストレーションに活用する方法についても学習します。最後は、MLflow を使用して機械学習の完全なライフサイクルを管理する方法の解説で締めくくります。
ML Pipelines on Google Cloud - 日本語版
ML Pipelines on Google Cloud - 日本語版Google Cloud
About this Course
What you will learn
TFX 標準パイプライン コンポーネントの概要を理解する
TFX Interactive Context を使用して TFX パイプラインのプロトタイプを開発する方法を理解する
TensorFlow、PyTorch、XGBoost、Scikit Learn モデルを使用した KubeFlow および AI Platform Pipelines での継続的なトレーニング
Composer と MLFlow を使用して継続的なトレーニングを実行する
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
このモジュールでは、TensorFlow Extended（TFX）を紹介し、TFX のコンセプトとコンポーネントについて説明します
TFX によるパイプライン オーケストレーション
TFX パイプラインのカスタム コンポーネントと CI / CD
このモジュールでは、アーティファクト管理のための TFX メタデータの活用について説明します。
複数の SDK、KubeFlow および AI Platform Pipelines を使用した継続的なトレーニング
このモジュールでは、複数の SDK、KubeFlow および AI Platform Pipelines を使用した継続的なトレーニングについて説明します。
Cloud Composer を使用した継続的なトレーニング
このモジュールでは、Cloud Composer を使用した継続的なトレーニングについて説明します。
MLflow を使用した ML パイプライン
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