Mastering Azure Machine Learning

Christoph Korner , Kaijisse Waaijer

Mastering Azure Machine Learning

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商品描述

Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow, Spark, and Kubernetes

Key Features

  • Make sense of data on the cloud by implementing advanced analytics
  • Train and optimize advanced deep learning models efficiently on Spark using Azure Databricks
  • Deploy machine learning models for batch and real-time scoring with Azure Kubernetes Service (AKS)

Book Description

The increase being seen in data volume today requires distributed systems, powerful algorithms, and scalable cloud infrastructure to compute insights and train and deploy machine learning (ML) models. This book will help you improve your knowledge of building ML models using Azure and end-to-end ML pipelines on the cloud.

The book starts with an overview of an end-to-end ML project and a guide on how to choose the right Azure service for different ML tasks. It then focuses on Azure ML and takes you through the process of data experimentation, data preparation, and feature engineering using Azure ML and Python. You'll learn advanced feature extraction techniques using natural language processing (NLP), classical ML techniques, and the secrets of both a great recommendation engine and a performant computer vision model using deep learning methods. You'll also explore how to train, optimize, and tune models using Azure AutoML and HyperDrive, and perform distributed training on Azure ML. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure ML, along with the basics of MLOps―DevOps for ML to automate your ML process as CI/CD pipeline.

By the end of this book, you'll have mastered Azure ML and be able to confidently design, build and operate scalable ML pipelines in Azure.

What you will learn

  • Setup your Azure ML workspace for data experimentation and visualization
  • Perform ETL, data preparation, and feature extraction using Azure best practices
  • Implement advanced feature extraction using NLP and word embeddings
  • Train gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure ML
  • Use hyperparameter tuning and AutoML to optimize your ML models
  • Employ distributed ML on GPU clusters using Horovod in Azure ML
  • Deploy, operate and manage your ML models at scale
  • Automated your end-to-end ML process as CI/CD pipelines for MLOps

Who this book is for

This machine learning book is for data professionals, data analysts, data engineers, data scientists, or machine learning developers who want to master scalable cloud-based machine learning architectures in Azure. This book will help you use advanced Azure services to build intelligent machine learning applications. A basic understanding of Python and working knowledge of machine learning are mandatory.

商品描述(中文翻譯)

掌握在 Azure 中使用 TensorFlow、Spark 和 Kubernetes 建立自動化及高度可擴展的端到端機器學習模型和管道的專家技術

主要特點

- 通過實施先進的分析來理解雲端數據
- 在 Spark 上使用 Azure Databricks 高效訓練和優化先進的深度學習模型
- 使用 Azure Kubernetes Service (AKS) 部署批次和即時評分的機器學習模型

書籍描述

當前數據量的增加需要分散式系統、強大的算法和可擴展的雲基礎設施來計算洞察並訓練和部署機器學習 (ML) 模型。本書將幫助您提升在 Azure 上構建 ML 模型及雲端端到端 ML 管道的知識。

本書首先概述了一個端到端的 ML 項目，並指導您如何為不同的 ML 任務選擇合適的 Azure 服務。接著專注於 Azure ML，帶您了解使用 Azure ML 和 Python 進行數據實驗、數據準備和特徵工程的過程。您將學習使用自然語言處理 (NLP)、經典 ML 技術的先進特徵提取技術，以及使用深度學習方法構建優秀推薦引擎和高效計算機視覺模型的秘密。您還將探索如何使用 Azure AutoML 和 HyperDrive 訓練、優化和調整模型，並在 Azure ML 上執行分散式訓練。然後，您將學習使用 Azure ML 和 Azure Kubernetes Services 的不同部署和監控技術，以及 MLOps 的基本概念——為 ML 自動化您的 ML 流程作為 CI/CD 管道。

在本書結束時，您將掌握 Azure ML，並能夠自信地設計、構建和運營可擴展的 ML 管道。

您將學到的內容

- 設置您的 Azure ML 工作區以進行數據實驗和可視化
- 使用 Azure 最佳實踐執行 ETL、數據準備和特徵提取
- 使用 NLP 和詞嵌入實施先進的特徵提取
- 在 Azure ML 上訓練梯度提升樹集成、推薦引擎和深度神經網絡
- 使用超參數調整和 AutoML 來優化您的 ML 模型
- 在 Azure ML 中使用 Horovod 在 GPU 集群上執行分散式 ML
- 大規模部署、運營和管理您的 ML 模型
- 將您的端到端 ML 流程自動化為 MLOps 的 CI/CD 管道

本書適合誰

這本機器學習書籍適合希望掌握 Azure 中可擴展雲端機器學習架構的數據專業人士、數據分析師、數據工程師、數據科學家或機器學習開發人員。本書將幫助您使用先進的 Azure 服務來構建智能機器學習應用程序。對 Python 的基本理解和機器學習的工作知識是必須的。

作者簡介

Christoph Körner recently worked as a Cloud Solution Architect for Microsoft specialised in Azure-based Big Data and Machine Learning solutions where he was responsible to design end-to-end Machine Learning and Data Science platforms. Since a few months, he works as a Senior Software Engineer at HubSpot, building a large-scale analytics platform. Before Microsoft, Christoph was the Technical Lead for Big Data at T-Mobile where his team designed, implemented and operated large-scale data, analytics and prediction pipelines on Hadoop. He also authored the 3 books: Deep Learning in the Browser (for Bleeding Edge Press), Learning Responsive Data Visualization and Data Visualization with D3 and AngularJS (both for Packt).

Kaijisse Waaijer is an experienced technologist, specializing in Data Platforms, Machine learning, and IoT. Kaijisse currently works for Microsoft EMEA as a Data Platform Consultant, specializing in Data Science, Machine learning and Big Data. She constantly works with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data creating better outcomes and business insights that drive value, using Microsoft technologies. Her true passion lies within the Trading Systems Automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.

作者簡介(中文翻譯)

Christoph Körner 最近擔任 Microsoft 的雲端解決方案架構師，專注於基於 Azure 的大數據和機器學習解決方案，負責設計端到端的機器學習和數據科學平台。幾個月前，他轉任 HubSpot 的高級軟體工程師，負責建構大型分析平台。在加入 Microsoft 之前，Christoph 是 T-Mobile 大數據的技術負責人，他的團隊設計、實施並運營基於 Hadoop 的大型數據、分析和預測管道。他還著有三本書：《Deep Learning in the Browser》（由 Bleeding Edge Press 出版）、《Learning Responsive Data Visualization》和《Data Visualization with D3 and AngularJS》（均由 Packt 出版）。

Kaijisse Waaijer 是一位經驗豐富的技術專家，專注於數據平台、機器學習和物聯網。Kaijisse 目前在 Microsoft EMEA 擔任數據平台顧問，專注於數據科學、機器學習和大數據。她不斷與多個行業的客戶合作，作為他們值得信賴的技術顧問，幫助他們優化組織數據，創造更好的結果和商業洞察，並利用 Microsoft 技術創造價值。她真正的熱情在於交易系統自動化，以及應用深度學習和神經網絡來實現高級預測和自動化。

目錄大綱

  1. Building an End-to-end Machine Learning Pipeline
  2. Choosing a Machine Learning Service in Azure
  3. Data Experimentation and Visualization using Azure
  4. ETL, Data Preparation and Feature Extraction
  5. Advanced Feature Extraction with NLP
  6. Building ML Models using Azure Machine Learning
  7. Training Deep Neural Networks on Azure
  8. Hyperparameter Tuning and Automated Machine Learning
  9. Distributed Machine Learning on Azure ML Clusters
  10. Building a Recommendation Engine in Azure
  11. Deploying and Operating Machine Learning Models
  12. MLOps – DevOps for Machine Learning
  13. What's next?

目錄大綱(中文翻譯)

1. 建立端到端的機器學習管道
2. 在 Azure 中選擇機器學習服務
3. 使用 Azure 進行數據實驗和可視化
4. ETL、數據準備和特徵提取
5. 使用 NLP 進行高級特徵提取
6. 使用 Azure Machine Learning 建立 ML 模型
7. 在 Azure 上訓練深度神經網絡
8. 超參數調整和自動化機器學習
9. 在 Azure ML 集群上進行分散式機器學習
10. 在 Azure 中建立推薦引擎
11. 部署和運行機器學習模型
12. MLOps – 機器學習的 DevOps
13. 接下來是什麼？

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