Azure Machine Learning Engineering

Dive into the world of Azure Machine Learning with this practical guide that enables you to build, deploy, and optimize machine learning models using Microsoft's leading cloud platform. You will gain hands-on experience with MLOps, model explainability, and distributed training to create scalable, production-ready ML solutions.

What this Book will help me do

• Train and manage machine learning models in Azure Machine Learning service.
• Deploy models on real-time scoring endpoints with scalable APIs.
• Apply techniques to mitigate bias in machine learning models.
• Build end-to-end machine learning pipelines using MLOps.
• Use tools like Azure Interpret for enhancing model explainability.

Author(s)

Dennis Michael Sawyers brings years of industry expertise in Azure environments, focusing on machine learning applications. Sina Fakhraee, Ph.D., is a seasoned data scientist passionate about bridging theory and practice in AI solutions. Balamurugan Balakreshnan, with a rich background in cloud systems, shares practical strategies for Azure enablement. Megan Masanz brings a unique approach to ML workflows, ensuring reliability and scalability.

Who is it for?

This book is perfect for machine learning engineers and data scientists who are eager to transition into ML engineering roles. Whether you're familiar with Azure or new to its ecosystem, this book will aid your journey towards mastering Azure Machine Learning. With a focus on actionable knowledge, it caters to professionals looking to build and deploy robust ML solutions.