Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML
by: Christoph Körner and Kaijisse Waaijer
Print Length 页数: 394 pages
Publisher finelybook 出版社: Packt Publishing (30 April 2020)
Language 语言: English
ISBN-10: 1789807557
ISBN-13: 9781789807554
Book Description
By finelybook
Master expert techniques for building automated and highly scalable end-to-end machine learning models and pipelines in Azure using TensorFlow,Spark,and Kubernetes
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
Table of Contents
Preface
Section 1: Azure Machine Learning Services
Chapter 1: Building an End-To-End Machine Learning Pipeline in Azure
Chapter 2: Choosing a Machine Learning Service in Azure
Section 2: Experimentation and Data Preparation
Chapter 3: Data Experimentation and Visualization Using Azure
Chapter 4: ETL,Data Preparation,and Feature Extraction
1 Chapter 5: Advanced Feature Extraction with NLP
Section 3: Training Machine Learning Models
1 Chapter 6: Building ML Models Using Azure Machine Learning
Chapter 7: Training Deep Neural Networks on Azure
Chapter 8: Hyperparameter Tuning and Automated Machine Learning
Chapter 9: Distributed Machine Learning on Azure ML Clusters
Chapter 10: Building a Recommendation Engine in Azure
Section 4: Optimization and Deployment of Machine Learning Models
1Chapter 11: Deploying and Operating Machine Learning Models
Chapter 12: MLOps-DevOps for Machine Learning
1 Chapter 13: What’s Next?
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