April 2026
Intermediate
586 pages
9h 14m
Japanese
Content preview from AutoMLを学ぶ (Japanese Edition)
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第4部：本番環境 とMLOps
正確なモデルを構築することは、戦いの半分に過ぎない。残りの半分——往々にしてより困難な部分——は、それらのモデルを本番環境に導入し、信頼性を持って稼働させ続け、周囲の状況が変化してもパフォーマンスを維持することだ。多くのAutoMLプロジェクトが失敗するのは、モデルの品質ではなく、運用上のギャップが原因である。
このパートでは、AutoMLとMLOpsの間のギャップを埋める。AutoMLをMLflowのような最新のMLプラットフォームに統合し、Apache Airflowで信頼性の高いデータパイプラインを構築し、ソフトウェアのデプロイと同等の厳格さでモデルのデプロイを扱うCI/CDプラクティスを実装する方法を学ぶ。これらは単なる理論上のベストプラクティスではない。毎日数百万件の予測を処理する本番システムで実戦検証されたアプローチだ。このパートの最後には、本番環境でAutoMLをデプロイし、維持するために必要な運用パターンがまとめられている。
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