October 2021
Intermediate to advanced
570 pages
9h 12m
Japanese
Content preview from 実践 AWSデータサイエンス ―エンドツーエンドのMLOpsパイプライン実装
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6章モデル訓練のためのデータセットの準備
前章では、Amazon SageMaker StudioとさまざまなPythonベースの可視化ライブラリを使用してデータセットを探索しました。Amazon Customer Reviews Datasetを使用して、商品カタログに関する重要なビジネス洞察を得ました。さらに、SageMaker Processing Job、Apache Spark、AWS Deequオープンソースライブラリを使用して、データセットの要約統計量を分析し、品質チェックを行いました。
本章では、「特徴量エンジニアリング」と呼ばれるプロセスで、人間が読めるテキストを機械が読めるベクトルに変換する方法について説明します。具体的には、Amazon Customer Reviews Datasetから生のreview_body列をBERTベクトルに変換します。これらのBERTベクトルを使って、「7章 最初のモデル訓練」と「8章 大規模なモデルの訓練と最適化」では、レビュー分類器モデルの訓練と最適化を行います。また、「7章 最初のモデル訓練」では、自然言語処理とBERTの起源について深く掘り下げます。
レビュー分類器モデルを使って、ソーシャルチャネルやパートナーサイトなどからの商品レビューのstar_ratingを予測します。レビューのstar_ratingを予測することで、製品管理チームやカスタマーサービスチームは、この予測を利用して、品質問題が公にエスカレーションされた際に、利用者からのメールや電話を待つことなく対処することができます。これにより、品質問題を検出するまでの平均時間は、数日/数ヶ月から数分/数時間に短縮されます。
6.1 特徴量選択と特徴量エンジニアリングを行う ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.