This book will help you to learn end to end Azure Machine Learning Service from the scratch starting with creation of the Workspace to Productionizing with MLOps. In between you will learn how to work with data stores along with training and tuning of the models and finally deploying them and productionizing the workload.

Azure Machine Learning Engineering is a comprehensive guide that provides an in-depth understanding of the Azure Machine Learning platform. This book covers everything from the basics of the platform to advanced machine learning tools available on Azure. The book is divided into different sections, each covering a specific aspect of the platform.The book's first section covers the basics of Azure Machine Learning and how to set up a development environment. The authors take the reader through the process of creating machine learning models, including how to use the platform's built-in tools and algorithms. The section also covers topics such as automated training and tuning of the models with AMLS.The second section of the book focuses on deployment. The authors provide a detailed explanation of the deployment of the various model via real-time inferencing and batch-scoring. The section also covers topics such as how to keep Responsible AI in consideration at all times.The third section of the book covers a detailed explanation of the productionalizing of the MLOps workload with Deep Learning in AML as well as Distributed Training in AMLS.Overall, This book is a comprehensive guide that provides a deep understanding of the Azure Machine Learning platform. The authors provide a clear and concise overview of the platform, making it accessible to both beginner and experienced data scientists and engineers. The book is well-organized and offers hands-on examples to help readers understand the concepts. If you are interested in learning more about Azure Machine Learning or are looking to implement machine learning models in the cloud, this book is an excellent resource.

Azure Machine Learning Engineering" is a comprehensive book that covers various aspects of building and deploying machine learning models using the Azure Machine Learning Service (AMLS). The book is divided into three parts, each focused on a specific area of the AMLS, namely, Training and Tuning Models, Deploying and Explaining Models, and Productionizing Your Workload with MLOps.Part 1 covers the basics of AMLS, including creating workspaces, setting up compute instances, working with data, training and tuning machine learning models using different techniques such as code-free models with designer, sampling hyperparameters, and Azure Automated Machine Learning. This section provides a good overview of the AMLS, and the chapters on Tuning Your Models with AMLS and Azure Automated Machine Learning are particularly useful for readers interested in hyperparameter tuning.Part 2 is dedicated to deploying and explaining machine learning models in AMLS. It covers deploying models for real-time inferencing and batch scoring, and how to use Responsible AI principles to evaluate and improve the model's performance. The chapters on Responsible AI and Productionizing Your Workload with MLOps provide valuable insights into the ethical considerations and practical aspects of deploying models in a production environment.Part 3 delves deeper into Productionizing Your Workload with MLOps. It covers using deep learning in AMLS, labeling image data, training object detection models using Azure Automated Machine Learning, and deploying the models to various environments.Some chapters that are worth reading are:Introducing the Azure Machine Learning Service: This chapter provides an overview of the Azure Machine Learning Service and its technical requirements, which is crucial for readers who are new to this service. It also covers building the first Azure Machine Learning workspace using different methods, which can be useful for readers who want to start using this service.Working with Data in AMLS: This chapter covers different methods of creating and managing data assets, including creating a blob storage account datastore using various methods, using Azure Machine Learning datasets, and reading data in a job. It is a must-read for readers who are interested in data processing and management using the Azure Machine Learning Service.Training Machine Learning Models in AMLS: This chapter explains how to train machine learning models using code-free models with the designer, creating a dataset using the user interface, and training on a compute instance or cluster. It also covers the summary of the chapter and the technical requirements needed for training models in the Azure Machine Learning Service.Tuning Your Models with AMLS: This chapter provides an overview of model parameters, sampling hyperparameters, and sweep jobs in the Azure Machine Learning Service. It also covers different policies for tuning models and setting up sweep jobs with different sampling methods, which can be useful for readers who want to optimize their models.Azure Automated Machine Learning: This chapter covers the introduction to Azure AutoML, featurization concepts in AML, and how to use AutoML using AMLS and the AML Python SDK. It also covers parsing AutoML results via AMLS and the AML SDK, which can be helpful for readers who want to use automated machine learning techniques.Deploying ML Models for Real-Time Inferencing: This chapter explains how to deploy an MLflow model with managed online endpoints through AML Studio or the Python SDK V2. It also covers deploying a model for real-time inferencing with managed online endpoints through the Azure CLI v2. It is a must-read for readers who want to deploy their machine learning models for real-time inferencing.Responsible AI: This chapter covers the responsible AI principles and toolbox overview. It also covers the responsible AI dashboard, error analysis dashboard, and interpretability dashboard, which can be helpful for readers who want to ensure that their models are responsible and ethical.Productionizing Your Workload with MLOps: This chapter explains how to implement MLOps in the Azure Machine Learning Service, including preparing the MLOps environment, creating an Azure DevOps organization and project, connecting to your AML workspace, moving code to the Azure DevOps repo, and creating an Azure DevOps pipeline. It also covers running an Azure DevOps pipeline and the summary and further reading of the chapterOverall, "Azure Machine Learning Engineering" is a well-written book that provides a comprehensive guide to building and deploying machine learning models using the AMLS. It covers a broad range of topics and provides detailed explanations with examples, making it suitable for both beginners and experienced practitioners in the field. The book is highly recommended for readers interested in using Azure Machine Learning Service for their machine learning projects

The book covers topics about Azure Machine Learning from training and tuning models with Azure Service, Deploying and Expanding Models and especially, product ionizing workload with MLOps. It is structured into distinct sections, each dedicated to a specific aspect of the platform. The authors' expertise, clear explanations, and practical code examples enhance the book's value, and a GitHub repository with code samples accompanies it.

Artificial Intelligence (AI) is taking all technologist to the new level. We can't imagine AI without Machine Learning (ML). Most learning comes in theoretical way. This is first book that came very handy to learn and apply learning through practical steps. Flow of the books is just perfect.I am grateful to the authors taking learning and apply towards curating perfect examples, content and writeup.