Data Science on AWS: Implementing End-To-End, Continuous AI and Machine Learning Pipelines (Paperback) (AWS上的數據科學：實現端到端的持續AI與機器學習管道)

Fregly, Chris, Barth, Antje

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

買這商品的人也買了...

相關主題

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)

這本實用書籍將教導AI和機器學習從業者如何在Amazon Web Services上成功建立和部署數據科學項目。Amazon的AI和機器學習堆棧統一了數據科學、數據工程和應用程序開發，以幫助提升您的技能水平。本指南將向您展示如何在雲端中構建和運行流程，然後在幾分鐘而不是幾天內將結果集成到應用程序中。在整本書中，作者Chris Fregly和Antje Barth演示了如何降低成本並提高性能。


  • 應用Amazon的AI和機器學習堆棧於自然語言處理、計算機視覺、欺詐檢測、對話設備等實際應用案例

  • 使用自動化機器學習來實現Amazon SageMaker Autopilot的特定子集應用案例

  • 深入探討基於BERT的NLP應用案例的完整模型開發生命周期，包括數據輸入、分析等

  • 將所有內容結合成可重複使用的機器學習操作流程

  • 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，位於加利福尼亞州舊金山。他還是位於舊金山的Advanced Spark、TensorFlow和KubeFlow Meetup系列的創始人。Chris經常在全球的AI和機器學習會議上演講，包括O'Reilly AI、Strata和Velocity會議。之前，Chris是PipelineAI的創始人，在那裡他與許多以AI為先的初創企業和企業合作，使用Apache Spark ML、Kubernetes、TensorFlow、Kubeflow、Amazon EKS和Amazon SageMaker持續部署ML/AI流程。他還是O'Reilly在線培訓系列《使用GPU進行高性能TensorFlow生產》的作者。

Antje Barth是AWS的AI和機器學習開發者倡導者，位於德國杜塞爾多夫。她還是杜塞爾多夫Women in Big Data Meetup的聯合創始人。Antje經常在全球的AI和機器學習會議和聚會上演講，包括O'Reilly AI和Strata會議。除了ML/AI，Antje還熱衷於幫助開發人員在AI和機器學習的背景下利用大數據、容器和Kubernetes平台。在加入AWS之前，Antje在MapR和Cisco擔任技術傳教士和解決方案工程角色。

類似商品