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Gen AI 時代における「サーバーレス」の価値を理解しよう / Value of Serve...

kensh
October 25, 2023

Gen AI 時代における「サーバーレス」の価値を理解しよう / Value of Serverless with genAI

サーバーレスを利用することにより、Gen AIにおけるコンシューマとしての価値が向上します。たとえば、以下の様なもの。

* イベント駆動アーキテクチャ により同期の待機時間が解消
* 進化的なアーキテクチャを促進します。
* コンポーネント単位のきめ細かいスケーリング
* 他のビジネス領域へのビジネス拡張も可能
* 障害の影響範囲の軽減

Gen AI 時代における「サーバーレス」の価値を理解し共有しましょう。

kensh

October 25, 2023
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  1. © 2023, Amazon Web Services, Inc. or its affiliates. All

    rights reserved. © 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved. Kensuke Shimokawa Snr. Serverless Specialist Amazon Web Services Japan Gen AI 時代における「サーバーレス」 の価値を理解しよう
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    rights reserved. © 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved. 2 Kensuke Shimokawa Amazon Web Services Japan Snr. Serverless Specialist Slides https://speakerdeck.com/_kensh Qiita https://qiita.com/_kensh
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    rights reserved. Serverless Serverless Application とは、 “誰も使用していないときは、 データ ストレージのコストを除いて、 実行に費用がかからない.” 5
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    rights reserved. 7 静的コンテンツ 動的データ 記事/試合状況 フォロー状況管理 認可 (フェデレーション) ログの保存 ETL処理 ログ収集 データ分析 クローリング 記事に対する処理 (タグ付けなど) 変更通知 画像に対する処理 (顔座標の検出) コードで実現 インテグレーションで実現
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    rights reserved. 10 Services Services Integration Event Driven
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    rights reserved. 11 Integration API Destination API Gateway Lambda Batch CloudWatch ECS EventBridge Bus Kinesis Data Firehose Inspector Kinesis Data Streams Redshift SageMaker pipeline SNS SQS Step Functions Event Driven
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    rights reserved. 12 Integration API Destination API Gateway Lambda Batch CloudWatch ECS EventBridge Bus Kinesis Data Firehose Inspector Kinesis Data Streams Redshift SageMaker pipeline SNS SQS Step Functions EventBridge Pipes Event Driven
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    rights reserved. 13 サーバーレス のサイズ感と組み立て
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    rights reserved. 14 VM Container Function
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    rights reserved. 15 VM Container Function たくさんのことを 一つのインスタンスで まぁまぁのことを 複数のインスタンスで ちょっとのことを たくさんのインスタンスで
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    rights reserved. 16 Function ちょっとのことを たくさんのインスタンスで たくさんの処理を ワークフローで
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    rights reserved. 17 Function ちょっとのことを たくさんのインスタンスで たくさんの処理を ワークフローで AWS Step Functions
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    rights reserved. 18 6 5 4 3 2 1 7 Array 56 6 5 4 3 2 1 7 MAP f (a) = 2 × a 12 10 8 6 4 2 14 Reduce g (b, c) = b + c
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    rights reserved. 20 6 5 4 3 2 1 7 Distibuted MAP f (a) = 2 × a 12 10 8 6 4 2 14 最大 10,000 の並列同時実行をサポート
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    rights reserved. 21 AI と Serverless
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    rights reserved. Amazon Confidential and Trademark. Gen AIとサーバーレスの統合 22 Prompt <Input at request time> Model Consumer 既存 Model + Model Tuner Domain Data + 新 Model Model Builder/Provider Internet Scale Dataset Or Domain Datasets + 新 Model Serverless Existing Model Time To Market
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    rights reserved. Amazon Confidential and Trademark. アジリティと Gen AI の組み合わせ 23 イノベーション をスピーディー に! Power of Gen AI Speed of Serverless Amazon Bedrock Amazon SageMaker Amazon Code Whisperer AWS Lambda Amazon ECS AWS Fargate Amazon EventBridge AWS Step Functions
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    rights reserved. Amazon Confidential and Trademark. 同期的なモデル呼び出し 24 • Amazon Bedrock • Amazon SageMaker Jumpstart • Models hosted on: • EKS • Other computes FM Endpoint • Auth, Rate limiting, Caching with Amazon API Gateway • AWS Lambda calling an endpoint API + Compute • Simple Q/A • Content generation • Text Summarization Use cases Amazon API Gateway AWS Lambda FM Endpoint
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    rights reserved. Amazon Confidential and Trademark. 非同期的なモデル呼び出し 25 • Amazon Bedrock • Amazon SageMaker Jumpstart • Models hosted on: • EKS • Other computes FM Endpoint • Auth, Rate limiting, Caching with Amazon API Gateway • Amazon SQS queue provides control over the scaling • AWS Lambda calling an endpoint API + Queue + Compute • Simple Q/A • Content generation • Text Summarization Use cases Amazon API Gateway AWS Lambda Amazon SQS FM Endpoint
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    rights reserved. Amazon Confidential and Trademark. 非同期の場合のレスポンス 26 • Amazon Bedrock • Amazon SageMaker Jumpstart • Models hosted on: • EKS • Other computes FM Endpoint • Auth, Rate limiting, Caching with Amazon API Gateway • Amazon SQS queue provides control over the scaling • AWS Lambda calling an endpoint • Use polling, WebSocket, or IoT core topic for response API + Queue + Compute • Simple Q/A • Content generation • Text Summarization Use cases Amazon API Gateway AWS Lambda Amazon SQS Polling/WebSocket FM Endpoint
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    rights reserved. Amazon Confidential and Trademark. Conversation History をどうするか? 27 • Amazon Bedrock • Amazon SageMaker Jumpstart • Models hosted on: • EKS • Other computes FM Endpoint • Auth, Rate limiting, Caching with Amazon API Gateway • Amazon SQS queue provides control over the scaling • AWS Lambda calling an endpoint • Use polling, WebSocket, or IoT core topic for response • Store conversation history API + Queue + Compute + DB • Simple Q/A • Content generation • Text Summarization • Conversation memory Use cases Amazon API Gateway AWS Lambda Amazon SQS Amazon DynamoDB FM Endpoint Polling/WebSocket
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    rights reserved. Amazon Confidential and Trademark. 複雑な工程は オーケストレーションする 28 • Amazon Bedrock • Amazon SageMaker Jumpstart • Models hosted on: • EKS • Other computes FM Endpoint • Auth, Rate limiting, Caching with Amazon API Gateway • Amazon SQS queue provides control over the scaling • AWS Lambda calling an endpoint • Use polling, WebSocket, or IoT core topic for response • Store conversation history • Co-ordinate multiple tasks with less code API + Queue + Compute + DB • Context based Q/A • Content generation • Text Summarization Use cases Amazon API Gateway AWS Lambda Amazon SQS Amazon DynamoDB AWS Step Functions workflow FM Endpoint Polling/WebSocket
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    rights reserved. Amazon Confidential and Trademark. 最近は、非同期レスポンスに IoT Core もよく使われる 29 AWS IoT Core Amazon SQS Amazon API Gateway AWS Step Functions workflow AWS Lambda
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    rights reserved. Amazon Confidential and Trademark. 複数モデルをコンテキストで使い分け 30 • Amazon Bedrock • Amazon SageMaker Jumpstart • Models hosted on: • EKS • Other computes FM Endpoint • Auth, Rate limiting, Caching with Amazon API Gateway • Amazon EventBridge or Amazon SNS to fan-out • Amazon SQS as subscribers • AWS Lambda or AWS Step Functions getting from the queue API + Fan-out (Pub/Sub) • Multi-model evaluation • Content generation from different models Use cases Amazon API Gateway AWS Step Functions Amazon EventBridge AWS Fargate Task AWS Lambda Amazon Bedrock ANTHROP\C Amazon Bedrock Amazon SageMaker Jumpstart Rule 1 Rule 2 Rule 3 Amazon SQS Amazon SQS Amazon SQS
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    rights reserved. Amazon Confidential and Trademark. 複数モデルをコンテキストで使い分け 31 • Amazon Bedrock • Amazon SageMaker Jumpstart • Models hosted on: • EKS • Other computes FM Endpoint • Auth, Rate limiting, Caching with Amazon API Gateway • AWS Step Functions invokes models in parallel and generates response • Introduce human-in-the- loop as an when needed API + Fan-out (Pub/Sub) • Multi-model evaluation • Content generation from different models Use cases Amazon API Gateway AWS Step Functions Amazon Bedrock ANTHROP\C Amazon Bedrock Amazon SageMaker Jumpstart
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    rights reserved. Amazon Confidential and Trademark. © 2023, Amazon Web Services, Inc. or its affiliates. All rights reserved. 32 RAG のための ベクトル埋込 A N S W E R Q U E S T I O N V E C T O R D A T A S T O R E S F O U N D A T I O N M O D E L Vector embeddings
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    rights reserved. Amazon Confidential and Trademark. データをベクトル埋め込みに変換する 33 Preprocess Chunk Create embeddings Store embeddings Embedding LLM Vector DB Documents AWS Step Functions workflow
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    rights reserved. Amazon Confidential and Trademark. RAG を使用した LLM の呼び出 34 • Amazon Bedrock • Amazon SageMaker Jumpstart • Embedding LLMs • Amazon Titan LLMs • Amazon OpenSearch • Amazon Kendra • Chroma DB • Pinecone Vector/Knowledge Databases • Domain specific Q/A • Intelligent Document Processing • Text Summarization Use cases • Native integration with Amazon S3 • Orchestration Serverless Retrieval Augmented Generation (RAG) Vector DB Documents Amazon S3 Amazon EventBridge Step Functions Amazon API Gateway AWS Lambda Amazon SQS AWS IoT Core ANTHROP\C Claude Amazon Bedrock Relevant Context Question? Answer Q? + C A Retrieval Augmented Generation (RAG) Similarity Search
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    rights reserved. 36 Serverless の今後?
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    rights reserved. Serverless の価値の今 • Serverless が担うビジネスロジック、ワークロードは広がる • Lambda が担ってきた “糊付け” としての役割は EventBridge や Step Functions に多く譲っていくことになりそう • genAIでは、TimeTM を意識して Consumer 部分はServerless で 37 今後の期待は、、 • 今は想像もしていないようなワークロードで Lambda が使われて いくのではないか? • 面白いユースケースがあれば、@_kensh まで DM ください
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    rights reserved. How to get started? 38 Document chat with AWS Lambda and Amazon Bedrock Enhancing AWS Intelligent document processing using Generative AI Generative AI with Serverless Multi-LLM and Multi-RAG powered chatbot using AWS CDK Generative AI for summarization and question answering using your own data