Mastering Azure Machine Learning

Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML

Christoph Korner , Kaijisse Waaijer 지음

Packt(GCO Science) 2020년 04월 30일 출간

작품소개

책소개

▶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

▶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)

▶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.

목차

▶TABLE of CONTENTS
▷ Section 1: Azure Machine Learning Services
1. Building an End-To-End Machine Learning Pipeline in Azure
2. Choosing a Machine Learning Service in Azure
▷ Section 2: Experimentation and Data Preparation
3. Data Experimentation and Visualization Using Azure
4. ETL, Data Preparation, and Feature Extraction
5. Advanced Feature Extraction with NLP
▷ Section 3: Training Machine Learning Models
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
▷ Section 4: Optimization and Deployment of Machine Learning Models
11. Deploying and Operating Machine Learning Models
12. MLOps - DevOps for Machine Learning
13. What's Next?

책 속으로

▶What this book covers
- Chapter 1, Building an End-to-End Machine Learning Pipeline in Azure, covers all the required components for running a custom end-to-end ML pipeline in Azure. Some sections might be a recap of your existing knowledge with useful practical tips, step-by-step guidelines, and pointers to Azure services to perform ML at scale. You can see it as an overview of the book, after which we will dive into every section in great detail with many practical examples and code throughout the remaining chapters of the book.

- Chapter 2, Choosing a Machine Learning Service in Azure, helps us to find out how to best navigate to all available ML services in Azure and how to select the right one for your goal. Finally, we will explain why Azure ML is the best choice for building custom ML models. This is the service that we will use throughout the book to implement an end-to-end ML pipeline.

- Chapter 3, Data Experimentation and Visualization Using Azure, takes a look at how to implement data experimentation and perform data visualizations with Azure ML. First, you will learn how to prepare and interact with your ML workspace. Once set up, you will be able to perform and track experiments in Azure, as well as trained models, plots, metrics, and snapshots of your code. This can all be done from your authoring Python environment, for example, Jupyter using Azure ML's Compute Instance or any Python interpreter running in PyCharm, VS Code, and so on. You will see many popular embeddings and visualization techniques including PCA, LDA, t-SNE, and UMAP in action.

- Chapter 4, ETL, Data Preparation, and Feature Extraction, explores data preparation and Extract, Transform, and Load (ETL) techniques within the Azure ML using Azure DataPrep. We will start by looking behind the scenes of datasets and data stores, the abstraction for physical data storage systems. Then, you will use Azure DataPrep to implement many popular preprocessing and feature engineering techniques such as imputing missing values, transformations, data type conversions, and many more. This will help you to implement a scalable ETL pipeline using Azure ML.

- Chapter 5, Advanced Feature Extraction with NLP, takes us one step further to extract features from textual and categorical data ？ a problem that users are faced often when training ML models. This chapter will describe the foundations of feature extraction with NLP. This will help the reader to create semantic embeddings from categorical and textual data using techniques including n-grams, Bag of Words, TF-IDF, Word2Vec, and more.

- Chapter 6, Building ML Models Using Azure Machine Learning, teaches you how to use ensembling techniques to build a traditional ML model in Azure. This chapter focuses on decision tree-based ensemble learning with popular state-of-the-art boosting and bagging techniques through the use of LightGBM in Azure ML. This will help you to apply concepts of bragging and boosting on ML models.

- Chapter 7, Training Deep Neural Networks on Azure, covers training more complex parametric models using deep learning for better generalization over large data. First, we will give a short and practical overview of when and why deep learning works well and its differences with traditional ML approaches. We will focus more on understanding rational, practical principles rather than a theoretical approach. Then we will train a Convolutional Neural Network (CNN) on Azure ML using Keras.

- Chapter 8, Hyperparameter Tuning and Automated Machine Learning, looks at optimizing the training process in order to take away some of the error-prone human choices from ML. These tuning tricks will help you to train better models, faster and more efficiently. First, we will look at hyperparameter tuning (also called HyperDrive in Azure ML), a standard technique for optimizing all parameter choices in an ML process. ...

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▶ Preface
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.

작가정보

저자(글) Christoph Korner

Christoph K？rner 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).

저자(글) Kaijisse Waaijer

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.