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Mastering Azure Machine Learning

You're reading from  Mastering Azure Machine Learning

Product type Book
Published in Apr 2020
Publisher Packt
ISBN-13 9781789807554
Pages 436 pages
Edition 1st Edition
Languages
Authors (2):
Christoph Körner Christoph Körner
Profile icon Christoph Körner
Kaijisse Waaijer Kaijisse Waaijer
Profile icon Kaijisse Waaijer
View More author details

Table of Contents (20) Chapters

Preface Section 1: Azure Machine Learning
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. Azure Machine Learning pipelines 6. Advanced feature extraction with NLP Section 3: Training Machine Learning Models
7. Building ML models using Azure Machine Learning 8. Training deep neural networks on Azure 9. Hyperparameter tuning and Automated Machine Learning 10. Distributed machine learning on Azure 11. Building a recommendation engine in Azure Section 4: Optimization and Deployment of Machine Learning Models
12. Deploying and operating machine learning models 13. MLOps—DevOps for machine learning 14. What's next? Index

Azure Machine Learning workspace

Azure Machine Learning is the newest member of the ML service family in Azure. It was initially built as an umbrella to combine all other ML services under a single workspace, and hence is also often referred to as the Azure Machine Learning workspace. Currently, it provides, combines, and abstracts many important ML infrastructure services and functionality such as tracking experiment runs and outputs, a model registry, an environment and container registry based on Conda and Docker, a dataset registry, pipelines, compute and storage infrastructure, and much more.

Besides all of the infrastructure services, it also integrates Azure Automated Machine Learning, Azure Machine Learning designer (, and a data-labeling UI in a single workspace that can share the same infrastructure resources. It is, in fact, the ML service that you are looking for if you want to do something serious. In many cases, it does all you can ask for and more. In this section...

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