Mastering Azure Machine Learning

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By Christoph Körner , Kaijisse Waaijer
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  1. Section 1: Azure Machine Learning

About this book

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 Machine Learning and takes you through the process of data experimentation, data preparation, and feature engineering using Azure Machine Learning 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 Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Then, you'll learn different deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, 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 Machine Learning and be able to confidently design, build and operate scalable ML pipelines in Azure.

Publication date:
April 2020


Section 1: Azure Machine Learning

In the first part of the book, the reader will come to understand the steps and requirements of an end-to-end machine learning pipeline and will be introduced to the different Azure Machine Learning. The reader will learn how to choose a machine learning service for a specific machine learning task.

This section comprises the following chapters:

  • Chapter 1, Building an end-to-end machine learning pipeline in Azure
  • Chapter 2, Choosing a machine learning service in Azure

About the Authors

  • Christoph Körner

    Christoph Körner recently worked as a cloud solution architect for Microsoft, specialising in Azure-based big data and machine learning solutions, where he was responsible to design end-to-end machine learning and data science platforms. For the last few months, he has been working 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 has also authored three books: Deep Learning in the Browser (for Bleeding Edge Press), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS (both for Packt).

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  • Kaijisse Waaijer

    Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create 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.

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