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AWS での Amazon SageMaker

Amazon SageMaker は、開発者やデータサイエンティストがあらゆる規模の機械學習モデルを短期間で簡単に構築、トレーニング、デプロイできるようにする完全マネージド型プラットフォームです。Amazon SageMaker を使用すると、通常、開発者による機械學習の足手まといになるような障壁をすべて取り除きます。

モデルを構築してトレーニングし、本番環境にデプロイするというプロセスは非常に複雑で多くの時間を必要とするため、ほとんどの開発者は機械學習は思っていたよりもずっと困難であると感じています。まず、トレーニングデータを収集して用意し、重要なデータセットの要素を検出する必要があります。次に、使用するアルゴリズムとフレームワークを選択する必要があります。アプローチを決定したら、予測方法をトレーニングによってモデルに教える必要がありますが、これには多くのコンピューティングが必要です。その後、最適な予測を実現するようモデルを調整する必要がありますが、ほとんどの場合、これには手間と手動による労力がかかります。完全にトレーニングされたモデルを開発したら、そのモデルをアプリケーションに統合し、次いでこのアプリケーションをインフラストラクチャにデプロイしてスケールします。このような作業すべてにおいて、多くの専門知識、大量のコンピューティングやストレージ、プロセスの各部分をテストして最適化するための多くの時間が必要とされます。結局、すべてを実行するのは不可能であるとほとんどの開発者が感じるのも不思議ではありません。

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