8 Best-Selling Azure Machine Learning Books Millions Love

Sumit Mund's extensive experience in predictive analytics shines through this guide to Azure Machine Learning Studio, designed to demystify the platform for new and intermediate users alike. You’ll start by exploring data visualization and preprocessing, then move on to constructing models using classification, regression, and clustering algorithms. Mund also guides you through deploying models as web service APIs and integrating R and Python code, illustrated with practical case studies. If you want a hands-on introduction that balances foundational knowledge with useful technical applications, especially for Azure's environment, this book gives you a straightforward path without assuming prior expertise.

Michael Washington's extensive experience in Azure Machine Learning and Angular development led him to write this guide for non-data scientists eager to build predictive models without deep data science expertise. You’ll learn how to create, deploy, and operationalize machine learning experiments using Azure Machine Learning Studio, Excel, and Angular .Net Core applications, including retraining models to improve accuracy over time. The book breaks down complex workflows into manageable steps, like setting up Azure workspaces, training and evaluating models, and integrating predictions into custom applications. If you want practical insights into making machine learning accessible beyond traditional data scientists, this book is tailored for you, though readers expecting exhaustive theory might find it focused more on applied techniques.

This tailored book explores proven Azure Machine Learning techniques carefully matched to your unique challenges and background. It examines key concepts such as model development, deployment, and operationalization, focusing on approaches that align with your specific goals and interests. Through a personalized lens, it reveals how to combine popular, battle-tested methods with your own context for deeper understanding and practical application. By concentrating on your individual learning needs, this book ensures you gain knowledge that resonates with your experience level and ambitions. It guides you through Azure ML’s core capabilities while addressing the nuances most relevant to your projects, making the learning experience both efficient and engaging.

After years of hands-on experience in data science and cloud analytics, Valentine Fontama developed this book to make predictive analytics accessible beyond traditional experts. You’ll learn how to build and deploy predictive models using Microsoft’s Azure Machine Learning service, starting from data ingestion through model evaluation to web service deployment. It dives into task-oriented examples, like creating predictive risk assessments and marketing models, all explained with minimal dependencies so you can focus on chapters relevant to your needs. If you’re looking to move past basic business intelligence and harness machine learning in a practical, cloud-based environment, this book offers a clear path without overwhelming jargon.

Deepak Mukunthu, with over 16 years in big data, analytics, and AI, leverages his expertise as principal program manager at Microsoft to demystify Automated Machine Learning on Azure. This book guides you through applying Automated ML to rapidly build machine learning models without the usual time investment, covering algorithm selection, auto featurization, and hyperparameter tuning. You'll find practical examples and case studies illustrating how diverse industries tackle real problems using these tools. If you're a data analyst, BI professional, or developer eager to integrate AI into your workflow efficiently, this book offers a clear pathway to get started and understand the nuances of Automated ML.

This book breaks down the complexities of building scalable machine learning pipelines on Microsoft's Azure platform. Kaijisse Waaijer, a seasoned Data Platform Consultant at Microsoft, draws from hands-on experience advising clients across industries to guide you through data preparation, advanced feature engineering like NLP, and training deep learning models using Azure Databricks and Kubernetes. Detailed chapters explain hyperparameter tuning with AutoML and distributed training with Horovod, equipping you to deploy and monitor models at scale using Azure Kubernetes Service. If you're fluent in Python and have foundational ML knowledge, this book offers concrete techniques to elevate your cloud-based machine learning projects efficiently.

This tailored book presents a focused, step-by-step plan for accelerating your mastery of Azure Machine Learning within 30 days. It explores key concepts, practical tools, and essential workflows tailored to your background and goals, ensuring you engage with content that truly matters to you. The book covers foundational topics like data preparation and model deployment while delving into advanced Azure ML features that match your interests and skill level. By concentrating on your specific objectives, this personalized guide reveals an efficient learning path that balances theory with hands-on practice, helping you build confidence and proficiency in Azure ML rapidly and effectively.

Ginger Grant, a Microsoft Certified Trainer and Data Scientist, brings her deep Azure expertise to this guide tailored for IT professionals aiming to master cloud data science. You learn how to prepare datasets, develop and deploy machine learning models, and manage Azure Machine Learning services effectively—all aligned with the objectives of Microsoft Exam 70-774. The book walks you through strategic what-if scenarios that sharpen your decision-making skills, especially in operationalizing models and integrating related services. If you're seasoned in Azure data services and want to elevate your machine learning proficiency, this book offers focused insights to bridge theory and practical exam readiness.

Parashar Shah, Thomas K Abraham, and Lauri Lehman bring their deep expertise in cloud technologies and machine learning to guide you through implementing AI solutions using Azure. You learn to apply Azure's Cognitive Services APIs, build and deploy models with Azure Machine Learning Studio, and leverage tools like SQL Server, HDInsight, and Spark for scalable data science projects. The book also explains integrating AI with IoT, streaming, and blockchain, offering practical insights across diverse applications. If you're a developer or data scientist familiar with programming languages like Python, R, or SQL, this book offers a clear path to mastering cloud-based machine learning environments.

The research was clear: traditional approaches to deploying machine learning models often faltered in scaling and monitoring, prompting Emmanuel Raj to write this guide grounded in his extensive industry experience. You’ll learn how to construct robust ML pipelines, implement CI/CD for automated deployments, and monitor models effectively for data and model drift within Azure environments. The book walks you through designing microservices and APIs tailored for production and test environments, supported by real-world project examples, making the dense topic accessible. This is a solid fit if you’re a machine learning engineer or DevOps professional aiming to operationalize ML solutions reliably at scale.