Machine Learning Platform Engineering: Build an Internal Developer Platform for ML and AI Systems
暫譯: 機器學習平台工程：為 ML 和 AI 系統構建內部開發者平台
Tan Wei Hao, Benjamin, Padmanabhan, Shanoop, Mallya, Varun
- 出版商: Manning
- 出版日期: 2026-03-10
- 售價: $2,350
- 貴賓價: 9.8 折 $2,303
- 語言: 英文
- 頁數: 504
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1633437337
- ISBN-13: 9781633437333
-
相關分類:
Machine Learning
海外代購書籍(需單獨結帳)
相關主題
商品描述
Get your machine learning models out of the lab and into production! Delivering a successful machine learning project is hard. Build a Machine Learning Platform (From Scratch) makes it easier. In it, you'll design a reliable ML system from the ground up, incorporating MLOps and DevOps along with a stack of proven infrastructure tools including Kubeflow, MLFlow, BentoML, Evidently, and Feast. In Build a Machine Learning Platform (From Scratch) you'll learn how to: - Set up an MLOps platform
- Deploy machine learning models to production
- Build end-to-end data pipelines
- Effective monitoring and explainability A properly designed machine learning system streamlines data workflows, improves collaboration between data and operations teams, and provides much-needed structure for both training and deployment. In Build a Machine Learning Platform (From Scratch) you'll learn how to design and implement a machine learning system from the ground up. You'll appreciate this instantly-useful introduction to achieving the full benefits of automated ML infrastructure. About the book Build a Machine Learning Platform (From Scratch) teaches you to set up and run a production-quality machine learning system using open source tools. Chapter-by-chapter, you'll assemble a delivery pipeline for an image classifier and a recommendation system, learning best practices as you go. Whether you're working with traditional models or tackling the creation of a cutting-edge transformer like the one detailed in Sebastian Raschka's Build a Large Language Model (From Scratch), this book provides the crucial MLOps framework to get it into production. You'll get hands-on experience with the most important parts of the machine learning workflow, including orchestrating pipelines; model training, inference, and serving; and monitoring and explainability. Soon, you'll be deploying models that are fast to production and easy to maintain and scale. About the reader For data scientists or software engineers who know how to program in Python. About the author Benjamin Tan is a product manager and principal engineer for sata Science at DKatalis where he leads a team of talented machine learning engineers, data scientists, and data engineers. He is also the author of The Little Elixir and OTP Guidebook and Building an ML Pipeline with Kubeflow (liveProject) from Manning, and Mastering Ruby Closures. Shanoop Padmanabhan is a software engineering manager at Continental Automotive, where he leads a team of software engineers focusing on machine learning based perception for autonomous vehicles. Varun Mallya is a machine learning engineer working at DKatalis where he is responsible for the setup and maintenance of the Bank's machine learning platform. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
- Deploy machine learning models to production
- Build end-to-end data pipelines
- Effective monitoring and explainability A properly designed machine learning system streamlines data workflows, improves collaboration between data and operations teams, and provides much-needed structure for both training and deployment. In Build a Machine Learning Platform (From Scratch) you'll learn how to design and implement a machine learning system from the ground up. You'll appreciate this instantly-useful introduction to achieving the full benefits of automated ML infrastructure. About the book Build a Machine Learning Platform (From Scratch) teaches you to set up and run a production-quality machine learning system using open source tools. Chapter-by-chapter, you'll assemble a delivery pipeline for an image classifier and a recommendation system, learning best practices as you go. Whether you're working with traditional models or tackling the creation of a cutting-edge transformer like the one detailed in Sebastian Raschka's Build a Large Language Model (From Scratch), this book provides the crucial MLOps framework to get it into production. You'll get hands-on experience with the most important parts of the machine learning workflow, including orchestrating pipelines; model training, inference, and serving; and monitoring and explainability. Soon, you'll be deploying models that are fast to production and easy to maintain and scale. About the reader For data scientists or software engineers who know how to program in Python. About the author Benjamin Tan is a product manager and principal engineer for sata Science at DKatalis where he leads a team of talented machine learning engineers, data scientists, and data engineers. He is also the author of The Little Elixir and OTP Guidebook and Building an ML Pipeline with Kubeflow (liveProject) from Manning, and Mastering Ruby Closures. Shanoop Padmanabhan is a software engineering manager at Continental Automotive, where he leads a team of software engineers focusing on machine learning based perception for autonomous vehicles. Varun Mallya is a machine learning engineer working at DKatalis where he is responsible for the setup and maintenance of the Bank's machine learning platform. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.
商品描述(中文翻譯)
將您的機器學習模型從實驗室帶入生產環境！
成功交付一個機器學習專案是困難的。從零開始建立機器學習平台 使這一過程變得更簡單。在本書中，您將從頭設計一個可靠的機器學習系統，結合 MLOps 和 DevOps，以及一系列經過驗證的基礎設施工具，包括 Kubeflow、MLFlow、BentoML、Evidently 和 Feast。 在 從零開始建立機器學習平台 中，您將學習如何： - 設置 MLOps 平台- 將機器學習模型部署到生產環境
- 建立端到端數據管道
- 有效的監控和可解釋性 一個設計良好的機器學習系統可以簡化數據工作流程，改善數據團隊和運營團隊之間的協作，並為訓練和部署提供急需的結構。在 從零開始建立機器學習平台 中，您將學習如何從頭設計和實施一個機器學習系統。您將會欣賞這個立即可用的介紹，幫助您充分利用自動化機器學習基礎設施的好處。 關於本書 從零開始建立機器學習平台 教您如何使用開源工具設置和運行生產級的機器學習系統。逐章組建一個圖像分類器和推薦系統的交付管道，並在過程中學習最佳實踐。無論您是使用傳統模型還是處理像 Sebastian Raschka 的 從零開始建立大型語言模型 中詳細介紹的尖端變壓器，本書都提供了將其投入生產所需的關鍵 MLOps 框架。您將獲得機器學習工作流程中最重要部分的實踐經驗，包括協調管道；模型訓練、推理和服務；以及監控和可解釋性。很快，您將能夠快速部署到生產環境且易於維護和擴展的模型。 關於讀者 適合會使用 Python 編程的數據科學家或軟體工程師。 關於作者 Benjamin Tan 是 DKatalis 的產品經理和數據科學首席工程師，他領導一支由優秀的機器學習工程師、數據科學家和數據工程師組成的團隊。他也是《The Little Elixir and OTP Guidebook》和《Building an ML Pipeline with Kubeflow (liveProject)》以及《Mastering Ruby Closures》的作者。 Shanoop Padmanabhan 是 Continental Automotive 的軟體工程經理，他領導一支專注於自動駕駛車輛的機器學習感知的軟體工程師團隊。 Varun Mallya 是 DKatalis 的機器學習工程師，負責銀行的機器學習平台的設置和維護。 購買印刷版書籍時，您將獲得 Manning 提供的免費電子書（PDF 或 ePub），以及訪問在線 liveBook 格式（及其 AI 助手，能用任何語言回答您的問題）的權限。
作者簡介
Benjamin Tan is a product manager and principal engineer for sata Science at DKatalis where he leads a team of talented machine learning engineers, data scientists, and data engineers. He is also the author of The Little Elixir and OTP Guidebook and Building an ML Pipeline with Kubeflow (liveProject) from Manning, and Mastering Ruby Closures. Shanoop Padmanabhan is a software engineering manager at Continental Automotive, where he leads a team of software engineers focusing on machine learning based perception for autonomous vehicles. Varun Mallya is a machine learning engineer working at DKatalis where he is responsible for the setup and maintenance of the Bank's machine learning platform.
作者簡介(中文翻譯)
班傑明·譚是DKatalis的產品經理及首席工程師，負責sata Science，領導一支由優秀的機器學習工程師、數據科學家和數據工程師組成的團隊。他也是The Little Elixir and OTP Guidebook和Building an ML Pipeline with Kubeflow（liveProject）以及Mastering Ruby Closures的作者。
沙努普·帕德馬納班是大陸汽車的軟體工程經理，領導一支專注於自動駕駛車輛的基於機器學習的感知技術的軟體工程師團隊。瓦倫·馬利亞是DKatalis的機器學習工程師，負責銀行的機器學習平台的設置和維護。
