Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform
暫譯: 將機器學習模型部署到生產環境：使用 Flask、Streamlit、Docker 和 Kubernetes 在 Google Cloud Platform 上
Singh, Pramod
- 出版商: Apress
- 出版日期: 2020-12-15
- 售價: $1,740
- 貴賓價: 9.5 折 $1,653
- 語言: 英文
- 頁數: 150
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1484265459
- ISBN-13: 9781484265451
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相關分類:
Docker、Flask、Google Cloud、Kubernetes、Machine Learning
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相關主題
商品描述
Build and deploy machine learning and deep learning models in production with end-to-end examples.
This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.
The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. What You Will Learn
Who This Book Is For
Data engineers, data scientists, analysts, and machine learning and deep learning engineers
This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes.
The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying them into production. It also offers guidance to those who want to move beyond Jupyter notebooks to training models at scale on cloud environments. All the code presented in the book is available in the form of Python scripts for you to try the examples and extend them in interesting ways. What You Will Learn
- Build, train, and deploy machine learning models at scale using Kubernetes
- Containerize any kind of machine learning model and run it on any platform using Docker
- Deploy machine learning and deep learning models using Flask and Streamlit frameworks
Who This Book Is For
Data engineers, data scientists, analysts, and machine learning and deep learning engineers
商品描述(中文翻譯)
構建和部署生產環境中的機器學習和深度學習模型，並提供端到端的範例。本書首先專注於機器學習模型的部署過程及其相關挑戰。接下來，涵蓋了使用不同的網頁框架（如 Flask 和 Streamlit）來構建和部署機器學習模型的過程。隨後有一章介紹 Docker，講解如何打包和容器化機器學習模型。本書還說明了如何使用 Kubernetes 在大規模上構建和訓練機器學習和深度學習模型。
本書是希望通過使用預構建模型並將其部署到生產環境中，來提升機器學習技能的人的良好起點。它還為那些希望超越 Jupyter notebooks，在雲端環境中進行大規模模型訓練的人提供指導。本書中呈現的所有代碼均以 Python 腳本的形式提供，供您嘗試範例並以有趣的方式擴展它們。
您將學到什麼
- 使用 Kubernetes 在大規模上構建、訓練和部署機器學習模型
- 容器化任何類型的機器學習模型，並使用 Docker 在任何平台上運行
- 使用 Flask 和 Streamlit 框架部署機器學習和深度學習模型
本書適合誰
數據工程師、數據科學家、分析師，以及機器學習和深度學習工程師
作者簡介
Pramod Singh is Manager of Data Science at Bain & Company. Previously, he worked as Sr. Machine Learning Engineer at Walmart Labs and Data Science Manager at Publicis Sapient in India. He has spent over 10 years working in machine learning, deep learning, data engineering, algorithm design, and application development. He has authored three Apress books: Machine Learning with PySpark, Learn PySpark, and Learn TensorFlow 2.0. He is a regular speaker at major conferences such as O'Reilly's Strata Data, GIDS, and other AI conferences. He is an active mentor and faculty in machine learning and AI at various educational institutes. He lives in Bangalore with his wife and four-year-old son. In his spare time, he enjoys playing guitar, coding, reading, and watching football. Manager of Data Science at Bain & Company. He has over 11 years of experience in the data science field working with multiple product- and service-based organizations. He has been part of numerous ML and AI large-scale projects. He has published three books on large scale data processing and machine learning. He is a regular speaker at major AI conferences.
作者簡介(中文翻譯)
Pramod Singh 是 Bain & Company 的數據科學經理。之前，他曾在 Walmart Labs 擔任資深機器學習工程師，並在印度的 Publicis Sapient 擔任數據科學經理。他在機器學習、深度學習、數據工程、算法設計和應用開發方面擁有超過 10 年的工作經驗。他是三本 Apress 書籍的作者：Machine Learning with PySpark、Learn PySpark 和 Learn TensorFlow 2.0。他是 O'Reilly 的 Strata Data、GIDS 及其他 AI 會議的常規演講者。他在多個教育機構中擔任機器學習和 AI 的活躍導師和講師。他與妻子和四歲的兒子住在班加羅爾。在空閒時間，他喜歡彈吉他、編程、閱讀和看足球。
他是 Bain & Company 的數據科學經理，在數據科學領域擁有超過 11 年的經驗，曾與多個產品和服務型組織合作。他參與了多個大型機器學習和 AI 項目，並出版了三本關於大規模數據處理和機器學習的書籍。他是主要 AI 會議的常規演講者。
