Mastering Azure Machine Learning
Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML
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.
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