D2-3-S09_秒間_6_万イベントの大規模リアルタイムデータを活用する機械学習基盤の実現__抜粋版_.pdf

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1. 牧野 祐己 CTO PLAID 2019 年 8 月 1日 D2-3-S09: 秒間 6 万イベントの大規模 リアルタイムデータを活用する 機械学習基盤の実現 ※本資料は当日登壇資料の一部を抜粋しています。
2. 02 リアルタイムデ ータ分析基盤と 学習基盤
3. 牧野 祐己 Chief Technology Officer
4. at GINZA SIX
5. Customer Experience Platform Real-time Analytics & Action Tool karte.io
6. どれくらいリアルタイムデータを 処理しているか？ 65,000 events (rows) / sec 3 billion events per day 2+ PB stored in BigQuery 1 PB streaming insert per month 0.x sec to real-time actions 60+ PB analysis per month 6,000 slots with flat Rate US & JP 500+ datasets for clients
7. データ分析基盤のアーキテクチャ Reactive Layer Track Compute Engine Autoscaling BigQuery Cloud Spanner Cloud Pub/Sub Cloud Dataflow Admin Compute Engine Autoscaling Analyze Compute Engine Autoscaling Cloud Bigtable 65,000 events / sec Redis Redis Cloud Bigtable Object Storage Salesforce Others... ELT Streaming Insert Client Cloud Storage 1 PB streaming insert per month Import / Export
8. データ活用における大事なポイント • とりあえずデータは全部突っ込んでから考える • データをどう使うか、から考える • 活用時はクライアントがコントローラブルであること • とりあえず活用してみて調整していく
9. 機械学習基盤で実現するライフサイクル 1. データ投入と前処理 2. 学習と検証 3. モデルのデプロイ 4. 予測とアクション そのサイクルをまわす Prepare Pre-process Ingest Train Evaluation Deploy Predict
10. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Prepare, Pre-process, Ingest Train, Evaluation Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy End-User End-User ML Architecture
11. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Prepare, Pre-process, Ingest ML Architecture
12. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Prepare, Pre-process, Ingest Train, Evaluation Batch Training Job Model Store ML Architecture
13. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Prepare, Pre-process, Ingest Train, Evaluation Batch Training Job Model Store Batch Predict Job Real-time Predict Service ML Architecture Deploy
14. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Prepare, Pre-process, Ingest Train, Evaluation Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy End-User End-User ML Architecture
15. 03 GCPによる実現 と活用事例
16. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Prepare, Pre-process, Ingest Train, Evaluation Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy End-User End-User ML Architecture
17. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy Predict End-User End-User Predict GCP で実現 Cloud Pub/Sub Cloud Bigtable BigQuery BigQuery BigQuery AI Platform Training Cloud AutoML BigQuery ML BigQuery Cloud Bigtable AI Platform Prediction Cloud AutoML API Cloud Dataflow Cloud Dataproc Cloud Dataprep BigQuery ELT Periodically Update Daily/Weekly /Monthly
18. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy Predict End-User End-User Predict GCP で実現 Cloud Pub/Sub Cloud Bigtable Periodically Update Daily/Weekly /Monthly
19. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy Predict End-User End-User Predict GCP で実現 Cloud Pub/Sub Cloud Bigtable Periodically Update Daily/Weekly /Monthly BigQuery BigQuery BigQuery BigQuery ELT
20. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy Predict End-User End-User Predict GCP で実現 Cloud Pub/Sub Cloud Bigtable Periodically Update Daily/Weekly /Monthly BigQuery BigQuery BigQuery BigQuery ELT Cloud Dataflow Cloud Dataproc Cloud Dataprep
21. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy Predict End-User End-User Predict GCP で実現 Cloud Pub/Sub Cloud Bigtable Periodically Update Daily/Weekly /Monthly BigQuery BigQuery BigQuery BigQuery ELT Cloud Dataflow Cloud Dataproc Cloud Dataprep AI Platform Training Cloud AutoML BigQuery ML
22. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy Predict End-User End-User Predict GCP で実現 Cloud Pub/Sub Cloud Bigtable Periodically Update Daily/Weekly /Monthly BigQuery BigQuery BigQuery BigQuery ELT Cloud Dataflow Cloud Dataproc Cloud Dataprep AI Platform Training Cloud AutoML BigQuery ML BigQuery Cloud Bigtable
23. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Batch Predict Job Real-time Predict Service Predict Key - Value Store Prediction Data Deploy Predict End-User End-User Predict GCP で実現 Cloud Pub/Sub Cloud Bigtable BigQuery BigQuery BigQuery AI Platform Training Cloud AutoML BigQuery ML BigQuery Cloud Bigtable AI Platform Prediction Cloud AutoML API Cloud Dataflow Cloud Dataproc Cloud Dataprep BigQuery ELT Periodically Update Daily/Weekly /Monthly
24. BigQueryを中心のデータストア とする機械学習基盤 Before BigQuery Cloud Dataflow BigQuery Cloud Bigtable Cloud Spanner AWS S3 Cloud Storage SFTP Treasure Data Salesforce Cloud Spreadsheet External Data ELT Import Export BigQuery
25. BigQueryを中心のデータストア とする機械学習基盤 After BigQuery Cloud Dataflow BigQuery Cloud Bigtable Cloud Spanner AWS S3 Cloud Storage SFTP Treasure Data Salesforce Cloud Spreadsheet External Data ELT Import Export BigQuery AI Platform Training Cloud AutoML BigQuery ML ML Predict
26. データ活用における大事なポイント • とりあえずデータは全部突っ込んでから考える • データをどう使うか、から考える • 活用時はクライアントがコントローラブルであること • とりあえず活用してみて調整していく
27. 事例 1: BigQuery ML k-means で ユーザーをクラスタリング サイトのユーザーを RFM パターンで クラスタリングする Recency: 最後に”購入”してからの時間 Frequency: ”購入”の頻度 Monetary Value: ”購入”の価値の大きさ CREATE OR REPLACE MODEL `cloud_next_session.rfm_cluster` OPTIONS (model_type='kmeans', num_clusters=4, standardize_features = TRUE) AS SELECT * FROM ( SELECT SUM(revenue) monetary, COUNT(*) frequency, MAX(_date) recency FROM `karte.events` WHERE event_name = 'buy' GROUP BY keys.visitor_id )
28. ユーザーをクラスタリングして セグメンテーションに利用
29. BI ツールで可視化
30. 事例 3: AI Platform で リアルタイムに購入予測 https://aaa.com/ category/items https://aaa.com/top https://aaa.com/ category/item/X XXXXXX CNN CNN CNN Embedding feature vector CNN True / False TensorFlow
31. 事例 3: AI Platform で リアルタイムに購入予測 BigQuery Cloud Dataflow Cloud Storage TensorFlow AI Platform Training Preprocess Train Deploy Ingest Realtime API
33. Real-time Offline Analyze Key - Value Store Data Lake Ingest Prep Training Data Store Feature Store Ingest Train & Eval Batch Training Job Model Store Real-time Predict Service Predict Deploy Predict End-User Cloud Pub/Sub Cloud Bigtable BigQuery BigQuery BigQuery AI Platform Training Cloud AutoML AI Platform Prediction Cloud AutoML API Cloud Dataflow Cloud Dataproc Cloud Dataprep BigQuery ELT Feature Feature Real-time Pattern
34. リアルタイム予測か、バッチ予測か？ リアルタイム予測 Pros - モデルの更新頻度が高い - 予測結果変動が大きい場合 - 必要な場合だけ計算 Cons - 使える Feature が限定的 バッチ予測 Pros - 複雑な Feature が利用できる - アーキテクチャがシンプル - 予測結果が確認できる Cons - モデルの更新頻度が低い - ありうる対象すべて計算しておく
35. 04 まとめ
36. まとめ リアルタイムデータ分 析基盤を元に機械学習 基盤を構築 GCP のさまざまなデ ータ、機械学習サービ スを利用してシンプル に実現 実際に活用している リアルタイム、バッチ の3事例を紹介
37. Thank you