Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow
暫譯: 建立機器學習管道：使用 TensorFlow 自動化模型生命週期

Hannes Hapke, Catherine Nelson

Building Machine Learning Pipelines: Automating Model Life Cycles with Tensorflow

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商品描述

Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems.

Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. The book also explores new approaches for integrating data privacy into machine learning pipelines.

  • Understand the machine learning management lifecycle
  • Implement data pipelines with Apache Airflow and Kubeflow Pipelines
  • Work with data using TensorFlow tools like ML Metadata, TensorFlow Data Validation, and TensorFlow Transform
  • Analyze models with TensorFlow Model Analysis and ship them with the TFX Model Pusher Component after the ModelValidator TFX Component confirmed that the analysis results are an improvement
  • Deploy models in a variety of environments with TensorFlow Serving, TensorFlow Lite, and TensorFlow.js
  • Learn methods for adding privacy, including differential privacy with TensorFlow Privacy and federated learning with TensorFlow Federated
  • Design model feedback loops to increase your data sets and learn when to update your machine learning models

 

 

 

商品描述(中文翻譯)

公司在機器學習專案上花費了數十億，但如果模型無法有效部署，這些錢就是浪費。在這本實用指南中，Hannes Hapke 和 Catherine Nelson 將帶您了解如何使用 TensorFlow 生態系統自動化機器學習管道的步驟。您將學習到能將部署時間從幾天縮短到幾分鐘的技術和工具，讓您能專注於開發新模型，而不是維護舊系統。

資料科學家、機器學習工程師和 DevOps 工程師將發現如何超越模型開發，成功將他們的資料科學專案商品化，而管理者則能更好地理解他們在加速這些專案中所扮演的角色。本書還探討了將資料隱私整合到機器學習管道中的新方法。

- 了解機器學習管理生命週期
- 使用 Apache Airflow 和 Kubeflow Pipelines 實現資料管道
- 使用 TensorFlow 工具（如 ML Metadata、TensorFlow Data Validation 和 TensorFlow Transform）處理資料
- 使用 TensorFlow Model Analysis 分析模型，並在 ModelValidator TFX 組件確認分析結果有所改善後，通過 TFX Model Pusher 組件發佈模型
- 使用 TensorFlow Serving、TensorFlow Lite 和 TensorFlow.js 在各種環境中部署模型
- 學習添加隱私的方法，包括使用 TensorFlow Privacy 的差異隱私和使用 TensorFlow Federated 的聯邦學習
- 設計模型反饋迴路以增加您的資料集，並學習何時更新您的機器學習模型

作者簡介

Hannes Hapke is a VP of Engineering at Caravel, a machine learning company providing novel personalization products for the retail industry. Prior to joining Caravel, Hannes was a Ssenior data science engineer at Cambia Health Solutions, a health solutions provider for 2.6 million people and a machine learning engineer at Talentpair, Inc., where he developed novel deep learning model for recruiting companies. Hannes cofounded a renewable energy startup which applied deep learning to detect homes would be optimal candidates for solar power.Additionally, Hannes has coauthored a publication about natural language processing and deep learning and presented at various conferences about deep learning and Python.

Catherine Nelson is a senior data scientist for Concur Labs at SAP Concur, where she explores innovative ways to use machine learning to improve the experience of a business traveller. She is particularly interested in privacy-preserving ML and applying deep learning to enterprise data. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University.

作者簡介(中文翻譯)

Hannes Hapke 是 Caravel 的工程副總裁，Caravel 是一家為零售業提供新穎個性化產品的機器學習公司。在加入 Caravel 之前，Hannes 曾擔任 Cambia Health Solutions 的高級數據科學工程師，該公司為 260 萬人提供健康解決方案，並在 Talentpair, Inc. 擔任機器學習工程師，為招聘公司開發新穎的深度學習模型。Hannes 共同創立了一家可再生能源初創公司，該公司應用深度學習來檢測哪些家庭最適合安裝太陽能。此外，Hannes 共同撰寫了一篇關於自然語言處理和深度學習的出版物，並在各種會議上就深度學習和 Python 進行了演講。

Catherine Nelson 是 SAP Concur 的 Concur Labs 的高級數據科學家，她探索創新的方法使用機器學習來改善商務旅行者的體驗。她特別對隱私保護的機器學習和將深度學習應用於企業數據感興趣。在她之前的職業生涯中，作為一名地球物理學家，她研究古代火山並在格林蘭進行石油勘探。Catherine 擁有達勒姆大學的地球物理學博士學位和牛津大學的地球科學碩士學位。

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