Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale

Raj, Emmanuel

  • 出版商: Packt Publishing
  • 出版日期: 2021-04-19
  • 售價: $1,650
  • 貴賓價: 9.5$1,568
  • 語言: 英文
  • 頁數: 370
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800562888
  • ISBN-13: 9781800562882
  • 相關分類: Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

Get up and running with machine learning life cycle management and implement MLOps in your organization


Key Features:

  • Become well-versed with MLOps techniques to monitor the quality of machine learning models in production
  • Explore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed models
  • Perform CI/CD to automate new implementations in ML pipelines


Book Description:

MLOps is a systematic approach to building, deploying, and monitoring machine learning (ML) solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with real-world examples to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production.


The book begins by familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then you'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll understand how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitoring pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects.


By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization.


What You Will Learn:

  • Formulate data governance strategies and pipelines for ML training and deployment
  • Get to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelines
  • Design a robust and scalable microservice and API for test and production environments
  • Curate your custom CD processes for related use cases and organizations
  • Monitor ML models, including monitoring data drift, model drift, and application performance
  • Build and maintain automated ML systems


Who this book is for:

This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.

商品描述(中文翻譯)

在您的組織中,掌握機器學習生命週期管理並實施MLOps。

主要特點:
- 熟悉MLOps技術,以監控生產中機器學習模型的品質
- 探索生產中ML模型的監控框架,並了解部署模型的端到端可追溯性
- 進行CI/CD以自動化ML流程中的新實施

書籍描述:
MLOps是一種系統化的方法,用於構建、部署和監控機器學習(ML)解決方案。它是一門工程學科,可應用於各種行業和用例。本書提供了關於MLOps的全面見解,並結合實際案例,幫助您編寫程序、訓練強大且可擴展的ML模型,並構建ML流程以安全地在生產環境中訓練和部署模型。

本書首先使您熟悉MLOps工作流程,以便您可以開始編寫程序來訓練ML模型。然後,您將探索在訓練後對ML模型進行序列化和打包以部署的選項,以促進機器學習推理、模型互操作性和端到端模型可追溯性。您將了解如何構建ML流程、持續集成和持續交付(CI/CD)流程以及監控流程,以系統地構建、部署、監控和管理企業和行業的ML解決方案。最後,您將應用所學知識來構建實際項目。

通過閱讀本書,您將全面了解MLOps,並準備好在組織中實施MLOps。

學到的知識:
- 制定數據治理策略和ML訓練和部署的流程
- 掌握實施ML流程、CI/CD流程和ML監控流程
- 設計測試和生產環境的強大且可擴展的微服務和API
- 為相關用例和組織定制自己的持續交付流程
- 監控ML模型,包括監控數據漂移、模型漂移和應用性能
- 構建和維護自動化的ML系統

本書適合數據科學家、軟件工程師、DevOps工程師、機器學習工程師以及商業和技術領導者,他們希望使用MLOps原則和技術在生產環境中構建、部署和維護ML系統。開始閱讀本書需要基本的機器學習知識。