Mastering Azure Machine Learning - Kujur, Bimal
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"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…mehr

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"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.
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