If you want to quickly get started with using Azure for machine learning, this book is a must-have. It’s suitable for learners at any level, from beginner to expert. You’ll learn something new or refresh your memory on important topics. I recently read this eBook and found it so useful that I bookmarked it as a reference for future use as I continue my own little personal dive into artificial intelligence. You can also choose to read our comprehensive and quiet detailed getting started with Azure Machine Learning guide.
What does Mastering Azure Machine Learning Cover?
With the rise in data volume, distributed systems, powerful algorithms, and scalable cloud infrastructure are necessary to extract insights and train and deploy machine learning (ML) models. This book will help you build ML models using Azure and create end-to-end ML pipelines on the cloud.
It begins with a general overview of an end-to-end ML project and guidance on choosing the appropriate Azure service for various 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 with NLP, classical ML techniques, and how to create a strong recommendation engine and a successful computer vision model using deep learning methods. You’ll also learn how to train, optimize, and tune models using Azure Automated Machine Learning and HyperDrive, and perform distributed training on Azure. Finally, you’ll discover various deployment and monitoring techniques using Azure Kubernetes Services with Azure Machine Learning, as well as the basics of MLOps, or DevOps for ML, to automate your ML process as a CI/CD pipeline.
By the end of this book, you’ll be proficient in Azure Machine Learning and able to confidently design, build, and operate scalable ML pipelines on Azure.
Chapter Previews of this book:
- Chapter 1: Building an end-to-end machine learning pipeline in Azure.
- Chapter 2: Choosing a machine learning service in Azure.
- Chapter 3: Data experimentation and visualization using Azure.
- Chapter 4: ETL, data preparation, and feature extraction.
- Chapter 5: Azure Machine Learning pipelines.
- Chapter 6: Advanced feature extraction with NLP.
- Chapter 7: Building ML models using Azure Machine Learning.
- Chapter 8: Training deep neural networks on Azure.
- Chapter 9: Hyperparameter tuning and Automated Machine Learning.
- Chapter 10: Distributed machine learning on Azure.
- Chapter 11: Building a recommendation engine in Azure.
- Chapter 12: Deploying and operating machine learning models.
- Chapter 13: MLOps – DevOps for machine learning.
- Chapter 14: What’s next?
Mastering Azure Machine Learning Links
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