Mastering Azure Machine Learning: Execute large-scale end-to-end machine learnin

Description: Mastering Azure Machine Learning by Christoph Korner, Marcel Alsdorf This updated second edition of Mastering Azure Machine Learning is a critically timed book. It builds upon simple and advanced NLP techniques, ML models such as boosted trees, deep neural network architectures, and more to help you leverage the power of Azure Machine Learning Services. FORMAT Paperback LANGUAGE English CONDITION Brand New Publisher Description Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning servicesKey FeaturesImplement end-to-end machine learning pipelines on AzureTrain deep learning models using Azure compute infrastructureDeploy machine learning models using MLOpsBook DescriptionAzure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, youll discover how to enrich and train ML models for embedding, classification, and regression. Youll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, youll be able to combine all the steps youve learned by building an MLOps pipeline.What you will learnUnderstand the end-to-end ML pipelineGet to grips with the Azure Machine Learning workspaceIngest, analyze, and preprocess datasets for ML using the Azure cloudTrain traditional and modern ML techniques efficiently using Azure MLDeploy ML models for batch and real-time scoringUnderstand model interoperability with ONNXDeploy ML models to FPGAs and Azure IoT EdgeBuild an automated MLOps pipeline using Azure DevOpsWho this book is forThis book is for machine learning engineers, data scientists, and machine learning developers who want to use the Microsoft Azure cloud to manage their datasets and machine learning experiments and build an enterprise-grade ML architecture using MLOps. This book will also help anyone interested in machine learning to explore important steps of the ML process and use Azure Machine Learning to support them, along with building powerful ML cloud applications. A basic understanding of Python and knowledge of machine learning are recommended. Author Biography Christoph Korner previously worked as a cloud solution architect for Microsoft, specializing in Azure-based big data and machine learning solutions, where he was responsible for designing end-to-end machine learning and data science platforms. He currently works for a large cloud provider on highly scalable distributed in-memory database services. Christoph has authored four books: Deep Learning in the Browser for Bleeding Edge Press, as well as Mastering Azure Machine Learning (first edition), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS for Packt Publishing.Marcel Alsdorf is a cloud solution architect with 5 years of experience at Microsoft consulting various companies on their cloud strategy. In this role, he focuses on supporting companies in their move toward being data-driven by analyzing their requirements and designing their data infrastructure in the areas of IoT and event streaming, data warehousing, and machine learning. On the side, he shares his technical and business knowledge as a coach in hackathons, as a mentor for start-ups and peers, and as a university lecturer. Before his current role, he worked as an FPGA engineer for the LHC project at CERN and as a software engineer in the banking industry. Table of Contents Table of ContentsUnderstanding the End-to-End Machine Learning ProcessChoosing the Right Machine Learning Service in AzurePreparing the Azure Machine Learning WorkspaceIngesting Data and Managing DatasetsPerforming Data Analysis and VisualizationFeature Engineering and LabelingAdvanced Feature Extraction with NLPAzure Machine Learning PipelinesBuilding ML Models Using Azure Machine LearningTraining Deep Neural Networks on AzureHyperparameter Tuning and Automated Machine LearningDistributed Machine Learning on AzureBuilding a Recommendation Engine in AzureModel Deployment, Endpoints, and OperationsModel Interoperability, Hardware Optimization, and IntegrationsBringing Models into Production with MLOpsPreparing for a Successful ML Journey Details ISBN1803232412 Author Marcel Alsdorf Short Title Mastering Azure Machine Learning Pages 624 Language English Year 2022 Edition 2nd ISBN-10 1803232412 ISBN-13 9781803232416 Format Paperback Publisher Packt Publishing Limited Imprint Packt Publishing Limited Place of Publication Birmingham Country of Publication United Kingdom AU Release Date 2022-05-27 NZ Release Date 2022-05-27 Edition Description 2nd Revised edition Publication Date 2022-05-27 Subtitle Execute large-scale end-to-end machine learning with Azure DEWEY 006.31 Audience General UK Release Date 2022-05-27 We've got this At The Nile, if you're looking for it, we've got it. 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