Data Science on AWS: Implementing End-To-End, Continuous AI and Machine Learning Pipelines (Paperback)
暫譯: 在AWS上進行資料科學：實現端到端的持續AI與機器學習管道（平裝本）

Fregly, Chris, Barth, Antje

Data Science on AWS: Implementing End-To-End, Continuous AI and Machine Learning Pipelines (Paperback)

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商品描述

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.

  • Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more
  • Use automated machine learning to implement a specific subset of use cases with Amazon SageMaker Autopilot
  • Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, and more
  • Tie everything together into a repeatable machine learning operations pipeline
  • Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka
  • Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

商品描述(中文翻譯)

這本實用的書籍將幫助人工智慧和機器學習的從業者學習如何在 Amazon Web Services 上成功構建和部署數據科學項目。Amazon 的人工智慧和機器學習技術堆疊統一了數據科學、數據工程和應用開發，幫助提升您的技能。本指南展示了如何在雲端構建和運行管道，然後在幾分鐘內將結果整合到應用程序中，而不是幾天。全書中，作者 Chris Fregly 和 Antje Barth 展示了如何降低成本並提高性能。

- 將 Amazon 的人工智慧和機器學習技術堆疊應用於自然語言處理、計算機視覺、詐騙檢測、對話設備等現實世界的使用案例
- 使用自動化機器學習來實現特定子集的使用案例，搭配 Amazon SageMaker Autopilot
- 深入探討基於 BERT 的自然語言處理使用案例的完整模型開發生命周期，包括數據攝取、分析等
- 將所有內容整合成可重複的機器學習運營管道
- 探索使用 Amazon Kinesis 和 Managed Streaming for Apache Kafka 在數據流上進行實時機器學習、異常檢測和流式分析
- 學習數據科學項目和工作流程的安全最佳實踐，包括身份和訪問管理、身份驗證、授權等

作者簡介

Chris Fregly is a Developer Advocate for AI and Machine Learning at AWS, based in San Francisco, California. He is also the founder of the Advanced Spark, TensorFlow, and KubeFlow Meetup Series based in San Francisco. Chris regularly speaks at AI and Machine Learning conferences across the world including the O'Reilly AI, Strata, and Velocity Conferences. Previously, Chris was Founder at PipelineAI where he worked with many AI-first startups and enterprises to continuously deploy ML/AI Pipelines using Apache Spark ML, Kubernetes, TensorFlow, Kubeflow, Amazon EKS, and Amazon SageMaker. He is also the author of the O'Reilly Online Training Series "High Performance TensorFlow in Production with GPUs"

Antje Barth is a Developer Advocate for AI and Machine Learning at AWS, based in Düsseldorf, Germany. She is also co-founder of the Düsseldorf chapter of Women in Big Data Meetup. Antje frequently speaks at AI and Machine Learning conferences and meetups around the world, including the O'Reilly AI and Strata conferences. Besides ML/AI, Antje is passionate about helping developers leverage Big Data, container and Kubernetes platforms in the context of AI and Machine Learning. Prior to joining AWS, Antje worked in technical evangelist and solutions engineering roles at MapR and Cisco.

作者簡介(中文翻譯)

Chris Fregly 是 AWS 的 AI 和機器學習開發者倡導者，駐紮於加州舊金山。他也是位於舊金山的 Advanced Spark、TensorFlow 和 KubeFlow Meetup 系列的創辦人。Chris 定期在全球各地的 AI 和機器學習會議上發言，包括 O'Reilly AI、Strata 和 Velocity 會議。之前，Chris 是 PipelineAI 的創辦人，與許多以 AI 為首的初創公司和企業合作，持續使用 Apache Spark ML、Kubernetes、TensorFlow、Kubeflow、Amazon EKS 和 Amazon SageMaker 部署 ML/AI 管道。他也是 O'Reilly 在線培訓系列《在生產環境中使用 GPU 的高效能 TensorFlow》的作者。

Antje Barth 是 AWS 的 AI 和機器學習開發者倡導者，駐紮於德國杜塞爾多夫。她也是杜塞爾多夫 Women in Big Data Meetup 的共同創辦人。Antje 經常在全球各地的 AI 和機器學習會議及 Meetup 上發言，包括 O'Reilly AI 和 Strata 會議。除了 ML/AI，Antje 對幫助開發者在 AI 和機器學習的背景下利用大數據、容器和 Kubernetes 平台充滿熱情。在加入 AWS 之前，Antje 曾在 MapR 和 Cisco 擔任技術傳道者和解決方案工程師。

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