Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
暫譯: 工程化 MLOps:快速構建、測試及管理可生產的機器學習生命週期
Raj, Emmanuel
- 出版商: Packt Publishing
- 出版日期: 2021-04-19
- 售價: $1,650
- 貴賓價: 9.5 折 $1,568
- 語言: 英文
- 頁數: 370
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1800562888
- ISBN-13: 9781800562882
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相關分類:
Machine Learning
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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)
開始運用機器學習生命週期管理並在您的組織中實施 MLOps
主要特點:
- 熟悉 MLOps 技術,以監控生產中機器學習模型的質量
- 探索生產中 ML 模型的監控框架,並了解已部署模型的端到端可追溯性
- 執行 CI/CD 以自動化 ML 管道中的新實現
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。這是一種可以應用於各種行業和用例的工程學科。本書提供了 MLOps 的全面見解,並結合實際案例,幫助您編寫程式、訓練穩健且可擴展的 ML 模型,並構建 ML 管道,以安全地在生產環境中訓練和部署模型。
本書首先讓您熟悉 MLOps 工作流程,以便您可以開始編寫程式來訓練 ML 模型。接著,您將探索在訓練後序列化和打包 ML 模型的選項,以便部署它們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 管道、持續集成和持續交付 (CI/CD) 管道,以及監控管道,以系統化地構建、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。
到本書結束時,您將對 MLOps 有全面的了解,並準備在您的組織中實施 MLOps。
您將學到的內容:
- 制定 ML 訓練和部署的數據治理策略和管道
- 掌握實施 ML 管道、CI/CD 管道和 ML 監控管道
- 設計穩健且可擴展的微服務和 API 以用於測試和生產環境
- 為相關用例和組織策劃自定義的 CD 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
本書適合誰:
這本 MLOps 書籍適合數據科學家、軟體工程師、DevOps 工程師、機器學習工程師以及希望使用 MLOps 原則和技術在生產中構建、部署和維護 ML 系統的商業和技術領導者。開始閱讀本書需要具備基本的機器學習知識。