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Mastering Azure Machine Learning
Mastering Azure Machine Learning

Mastering Azure Machine Learning: Execute large-scale end-to-end machine learning with Azure , Second Edition

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Profile Icon Christoph Körner Profile Icon Marcel Alsdorf
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$43.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (15 Ratings)
Paperback May 2022 624 pages 2nd Edition
eBook
$9.99 $39.99
Paperback
$43.99
Arrow left icon
Profile Icon Christoph Körner Profile Icon Marcel Alsdorf
Arrow right icon
$43.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5 (15 Ratings)
Paperback May 2022 624 pages 2nd Edition
eBook
$9.99 $39.99
Paperback
$43.99
eBook
$9.99 $39.99
Paperback
$43.99

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Mastering Azure Machine Learning

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Key benefits

  • Implement end-to-end machine learning pipelines on Azure
  • Train deep learning models using Azure compute infrastructure
  • Deploy machine learning models using MLOps

Description

Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you’ll be able to combine all the steps you’ve learned by building an MLOps pipeline.

Who is this book for?

This book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended.

What you will learn

  • Understand the end-to-end ML pipeline
  • Get to grips with the Azure Machine Learning workspace
  • Ingest, analyze, and preprocess datasets for ML using the Azure cloud
  • Train traditional and modern ML techniques efficiently using Azure ML
  • Deploy ML models for batch and real-time scoring
  • Understand model interoperability with ONNX
  • Deploy ML models to FPGAs and Azure IoT Edge
  • Build an automated MLOps pipeline using Azure DevOps
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Publication date, Length, Edition, Language, ISBN-13
Publication date : May 10, 2022
Length: 624 pages
Edition : 2nd
Language : English
ISBN-13 : 9781803232416
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Product Details

Publication date : May 10, 2022
Length: 624 pages
Edition : 2nd
Language : English
ISBN-13 : 9781803232416
Category :
Tools :

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Frequently bought together


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Table of Contents

22 Chapters
Section 1: Introduction to Azure Machine Learning Chevron down icon Chevron up icon
Chapter 1: Understanding the End-to-End Machine Learning Process Chevron down icon Chevron up icon
Chapter 2: Choosing the Right Machine Learning Service in Azure Chevron down icon Chevron up icon
Chapter 3: Preparing the Azure Machine Learning Workspace Chevron down icon Chevron up icon
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining Chevron down icon Chevron up icon
Chapter 4: Ingesting Data and Managing Datasets Chevron down icon Chevron up icon
Chapter 5: Performing Data Analysis and Visualization Chevron down icon Chevron up icon
Chapter 6: Feature Engineering and Labeling Chevron down icon Chevron up icon
Chapter 7: Advanced Feature Extraction with NLP Chevron down icon Chevron up icon
Chapter 8: Azure Machine Learning Pipelines Chevron down icon Chevron up icon
Section 3: The Training and Optimization of Machine Learning Models Chevron down icon Chevron up icon
Chapter 9: Building ML Models Using Azure Machine Learning Chevron down icon Chevron up icon
Chapter 10: Training Deep Neural Networks on Azure Chevron down icon Chevron up icon
Chapter 11: Hyperparameter Tuning and Automated Machine Learning Chevron down icon Chevron up icon
Chapter 12: Distributed Machine Learning on Azure Chevron down icon Chevron up icon
Chapter 13: Building a Recommendation Engine in Azure Chevron down icon Chevron up icon
Section 4: Machine Learning Model Deployment and Operations Chevron down icon Chevron up icon
Chapter 14: Model Deployment, Endpoints, and Operations Chevron down icon Chevron up icon
Chapter 15: Model Interoperability, Hardware Optimization, and Integrations Chevron down icon Chevron up icon
Chapter 16: Bringing Models into Production with MLOps Chevron down icon Chevron up icon
Chapter 17: Preparing for a Successful ML Journey Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(15 Ratings)
5 star 86.7%
4 star 0%
3 star 0%
2 star 0%
1 star 13.3%
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Jagannath Banerjee May 27, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Mastering Azure Machine Learning (Second Edition) is an excellent book of reference for anyone working on AI/ML in Azure Platform. This 600 page book will beat Azure ML documentation any day :). Book covers entire lifecycle from concept to implementation for Machine Learning, NLP, Recommender system and Deep Learning with right amount of theory and code (Python).Authors start with concepts and life cycle of a Machine learning project, followed by creating workspace in Azure, choose correct ML Services (Studio/Auto ML/Designer) , data preparation, ingestion thru various methods, data visualization , feature engineering, feature extraction for NLP, hyper-parameter tuning and deployment and generate endpoints for consumptions.ML Model value add is hard to quantify unless its deployed. That leads to my favorite chapters :Ch-14 : Model Deployment, Endpoints and Operations - This chapters covers model deployment for batch & real time in great details. Explains how to register a model, define compute clusters for scoring, install packages, create contain instances and deploy in AKS.Ch:16 : Bringing Models into production using MLOps. This chapter explains deployment using CI/CD pipeline in Azure DevOps. Deployment concepts have been explained in great details. For the next edition, I would love to see an end to end pipeline scripts considering the environment, variables, keys and secrets into actions in the pipeline yml file.I would suggest this book for someone with basic understanding of Azure Environment, Python, Machine Learning.Overall fantastic effort from authors and great book!
Amazon Verified review Amazon
Patrick Hansen May 10, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Comprehensive overview of how to build an end to end machine learning pipeline on Azure. Great hands-on examples that allow the reader to follow the process with detailed descriptions and code snippets. Well done!
Amazon Verified review Amazon
Niklas Fehrenbach Jun 12, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Disclaimer: I have received an advanced copy from the publisherThe second edition of “Mastering Azure Machine Learning” addresses a broad audience. It is suitable for readers that are interested in the subject of Machine Learning (like me) but also for advanced data scientists who would like to develop enterprise-grade models on Azure.The goal of the first chapters is to bring the various readers on the same level of knowledge. The authors for example describe the statistical & mathematical foundations, the end-to-end ML process and the relevance of MLOps. There is also a comparison of the various AI services that are available on Azure.The remaining and major part of the book is dedicated to the Azure Machine Learning service. The authors deep dive in every step of the end-to-end ML process and implement it using the comprehensive features of the service. Topics that are discussed in detail are for example ingestion storages, exploration techniques, building deep learning models, hyperparameter tuning and deployment targets.What I like most about the book is the clear structure and the well-chosen mix of concept-explanation and hands-on implementation. If required, the authors explain the underlying theories to help readers pick up. Advanced Data Scientists can easily skip these parts and jump straight to the implementation. All code snippets are available in a GitHub repository which makes it convenient to follow the instructions in your own Azure subscription.I can highly recommend this book!
Amazon Verified review Amazon
Max Kirschning May 28, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book gives the reader a well-written overview of the full process of azure machine learning. The authors enable the reader by starting with the core ml basics and then show how they are implemented in the Azure environment.The book is a useful read for professionals and starters alike as it can be used as a full-blown guide and introduction, as well as a reference.
Amazon Verified review Amazon
Adalberto Teran Oct 14, 2022
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A must have/read piece for all Azure AI/ML professionals.Easy to navigate and to find the right resource, either you are learning it or just refreshing your mind for almost any needed topic: From data and environment preparation, feature engineering/labeling/advance extraction, pipeline design, model design, optimization, training, deployment , operations/producton.It covers from traditional models using basic classifiers and its optimization (it does not go deeper with classifiers analysis but it covers what you will need for most of the time... men you can not have it all in just one book right?) to NLP, CNN, and other important needed topics...It gets your project covered end to end...Good investment of time and money!
Amazon Verified review Amazon
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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

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  8. Guinea-bissau
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  14. Russian Federation
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