Machine Learning Engineering with MLflow: Manage the end-to-end machine learning life cycle with MLflow

Lauchande, Natu

  • 出版商: Packt Publishing
  • 出版日期: 2021-08-27
  • 售價: $1,480
  • 貴賓價: 9.5$1,406
  • 語言: 英文
  • 頁數: 248
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1800560796
  • ISBN-13: 9781800560796
  • 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, and productive in no time with MLflow using the most effective machine learning engineering approach


Key Features:

  • Explore machine learning workflows for stating ML problems in a concise and clear manner using MLflow
  • Use MLflow to iteratively develop a ML model and manage it
  • Discover and work with the features available in MLflow to seamlessly take a model from the development phase to a production environment


Book Description:

MLflow is a platform for the machine learning life cycle that enables structured development and iteration of machine learning models and a seamless transition into scalable production environments.


This book will take you through the different features of MLflow and how you can implement them in your ML project. You will begin by framing an ML problem and then transform your solution with MLflow, adding a workbench environment, training infrastructure, data management, model management, experimentation, and state-of-the-art ML deployment techniques on the cloud and premises. The book also explores techniques to scale up your workflow as well as performance monitoring techniques. As you progress, you'll discover how to create an operational dashboard to manage machine learning systems. Later, you will learn how you can use MLflow in the AutoML, anomaly detection, and deep learning context with the help of use cases. In addition to this, you will understand how to use machine learning platforms for local development as well as for cloud and managed environments. This book will also show you how to use MLflow in non-Python-based languages such as R and Java, along with covering approaches to extend MLflow with Plugins.


By the end of this machine learning book, you will be able to produce and deploy reliable machine learning algorithms using MLflow in multiple environments.


What You Will Learn:

  • Develop your machine learning project locally with MLflow's different features
  • Set up a centralized MLflow tracking server to manage multiple MLflow experiments
  • Create a model life cycle with MLflow by creating custom models
  • Use feature streams to log model results with MLflow
  • Develop the complete training pipeline infrastructure using MLflow features
  • Set up an inference-based API pipeline and batch pipeline in MLflow
  • Scale large volumes of data by integrating MLflow with high-performance big data libraries


Who this book is for:

This book is for data scientists, machine learning engineers, and data engineers who want to gain hands-on machine learning engineering experience and learn how they can manage an end-to-end machine learning life cycle with the help of MLflow. Intermediate-level knowledge of the Python programming language is expected.

商品描述(中文翻譯)

立即使用最有效的機器學習工程方法,快速上手並提高生產力,使用MLflow。

主要特點:
- 使用MLflow以簡潔明確的方式探索機器學習工作流程,解決機器學習問題
- 使用MLflow進行迭代開發和管理機器學習模型
- 利用MLflow提供的功能,無縫將模型從開發階段轉移到生產環境

書籍描述:
MLflow是一個機器學習生命周期平台,可以實現結構化的機器學習模型開發和迭代,並無縫過渡到可擴展的生產環境。

本書將介紹MLflow的不同功能以及如何在機器學習項目中應用它們。您將從定義機器學習問題開始,然後使用MLflow進行轉換,添加工作台環境、訓練基礎設施、數據管理、模型管理、實驗和最先進的雲端和本地機器學習部署技術。本書還探討了擴展工作流程和性能監控技術的技巧。隨著您的進展,您將發現如何創建一個操作儀表板來管理機器學習系統。此外,您還將學習如何在AutoML、異常檢測和深度學習上下文中使用MLflow,並通過使用案例了解如何在R和Java等非Python語言中使用MLflow,以及擴展MLflow的方法。

通過閱讀本書,您將能夠在多個環境中使用MLflow生成並部署可靠的機器學習算法。

學到什麼:
- 使用MLflow的不同功能在本地開發機器學習項目
- 設置集中式的MLflow跟踪服務器,管理多個MLflow實驗
- 通過創建自定義模型,使用MLflow建立模型生命周期
- 使用特徵流在MLflow中記錄模型結果
- 使用MLflow功能開發完整的訓練管道基礎設施
- 在MLflow中設置基於推理的API管道和批處理管道
- 通過將MLflow與高性能大數據庫集成,處理大量數據

本書適合數據科學家、機器學習工程師和數據工程師,他們希望獲得實踐機器學習工程的經驗,並學習如何使用MLflow管理端到端的機器學習生命周期。預期讀者具備中級水平的Python編程知識。