Mastering Azure Machine Learning (Paperback)

Bimal Kujur

ISBN 13: 9798307432532

Published by Independently Published, 2025

About this Item

Paperback. "Mastering Azure Machine Learning: From Fundamentals to Deployment" is designed to bridge the gap between foundational ML concepts and the complexities of deploying production-ready systems. While Azure ML's intuitive interface and robust APIs simplify many tasks, mastery of the platform requires an understanding of the underlying concepts, tools, and workflows. This book equips you with the skills and knowledge to navigate every stage of the machine learning lifecycle using Azure ML-from data preparation and model development to deployment, monitoring, and scaling.This book caters to a broad audience: Beginners: Individuals new to machine learning who want a structured guide to mastering Azure ML's features while understanding fundamental ML concepts.Intermediate Practitioners: Those with some experience in ML who wish to deepen their knowledge of Azure ML's advanced capabilities, such as AutoML, distributed training, and pipeline orchestration.Professionals: Industry experts seeking insights into implementing scalable and efficient ML solutions in real-world scenarios.Regardless of your starting point, this book emphasizes practical examples, clear explanations, and actionable insights to accelerate your Azure ML journey.What You Will LearnThe book's modular structure ensures a comprehensive learning experience: Foundations: Introduction to Azure ML, its architecture, and core capabilities.Model Development: Hands-on guidance for building and training models, including data preprocessing, experimentation, and hyperparameter tuning.AutoML and Responsible AI: Insights into automation, interpretability, and fairness to ensure ethical and impactful solutions.Advanced Techniques: Distributed training, embedding models, and working with large language models.Deployment: Real-world approaches to deploying, monitoring, and scaling ML systems on Azure.Case Studies and Best Practices: Success stories across industries and actionable best practices for leveraging Azure ML effectively. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9798307432532

Bibliographic Details

• Title: Mastering Azure Machine Learning (Paperback)
• Publisher: Independently Published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798307432532
• Binding: Paperback
• Condition: new

About this title

Synopsis

“Mastering Azure Machine Learning: From Fundamentals to Deployment” is designed to bridge the gap between foundational ML concepts and the complexities of deploying production-ready systems. While Azure ML’s intuitive interface and robust APIs simplify many tasks, mastery of the platform requires an understanding of the underlying concepts, tools, and workflows. This book equips you with the skills and knowledge to navigate every stage of the machine learning lifecycle using Azure ML—from data preparation and model development to deployment, monitoring, and scaling.
This book caters to a broad audience:

• Beginners: Individuals new to machine learning who want a structured guide to mastering Azure ML’s features while understanding fundamental ML concepts.
• Intermediate Practitioners: Those with some experience in ML who wish to deepen their knowledge of Azure ML’s advanced capabilities, such as AutoML, distributed training, and pipeline orchestration.
• Professionals: Industry experts seeking insights into implementing scalable and efficient ML solutions in real-world scenarios.

Regardless of your starting point, this book emphasizes practical examples, clear explanations, and actionable insights to accelerate your Azure ML journey.
What You Will Learn
The book’s modular structure ensures a comprehensive learning experience:

1. Foundations: Introduction to Azure ML, its architecture, and core capabilities.
2. Model Development: Hands-on guidance for building and training models, including data preprocessing, experimentation, and hyperparameter tuning.
3. AutoML and Responsible AI: Insights into automation, interpretability, and fairness to ensure ethical and impactful solutions.
4. Advanced Techniques: Distributed training, embedding models, and working with large language models.
5. Deployment: Real-world approaches to deploying, monitoring, and scaling ML systems on Azure.
6. Case Studies and Best Practices: Success stories across industries and actionable best practices for leveraging Azure ML effectively.