Mastering Azure Machine Learning
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Mastering Azure Machine Learning
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Overview of 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.
Table of Contents (20 chapters)
Section 1: Azure Machine Learning
Free Chapter
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
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