Azure Machine Learning Engineering: Deploy, fine-tune, and optimize ML models us

Description: Azure Machine Learning Engineering by Sina Fakhraee, Balamurugan Balakreshnan, Megan Masanz Data scientists working with Azure will be able to put their knowledge to work with this practical guide to Azure Machine Learning focused on leveraging the SDK V2 and CLI V2. With detailed steps and explanations, youll learn how to build and productionize end-to-end machine learning solutions using Azure ML. FORMAT Paperback CONDITION Brand New Publisher Description Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning ServiceKey FeaturesAutomate complete machine learning solutions using Microsoft AzureUnderstand how to productionize machine learning modelsGet to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learningBook DescriptionData scientists working on productionizing machine learning (ML) workloads face a breadth of challenges at every step owing to the countless factors involved in getting ML models deployed and running. This book offers solutions to common issues, detailed explanations of essential concepts, and step-by-step instructions to productionize ML workloads using the Azure Machine Learning service. Youll see how data scientists and ML engineers working with Microsoft Azure can train and deploy ML models at scale by putting their knowledge to work with this practical guide.Throughout the book, youll learn how to train, register, and productionize ML models by making use of the power of the Azure Machine Learning service. Youll get to grips with scoring models in real time and batch, explaining models to earn business trust, mitigating model bias, and developing solutions using an MLOps framework.By the end of this Azure Machine Learning book, youll be ready to build and deploy end-to-end ML solutions into a production system using the Azure Machine Learning service for real-time scenarios.What you will learnTrain ML models in the Azure Machine Learning serviceBuild end-to-end ML pipelinesHost ML models on real-time scoring endpointsMitigate bias in ML modelsGet the hang of using an MLOps framework to productionize modelsSimplify ML model explainability using the Azure Machine Learning service and Azure InterpretWho this book is forMachine learning engineers and data scientists who want to move to ML engineering roles will find this AMLS book useful. Familiarity with the Azure ecosystem will assist with understanding the concepts covered. Author Biography Sina Fakhraee, Ph.D., is currently working at Microsoft as an enterprise data scientist and senior cloud solution architect. He has helped customers to successfully migrate to Azure by providing best practices around data and AI architectural design and by helping them implement AI/ML solutions on Azure. Prior to working at Microsoft, Sina worked at Ford Motor Company as a product owner for Fords AI/ML platform. Sina holds a Ph.D. degree in computer science and engineering from Wayne State University and prior to joining the industry, he taught various undergrad and grad computer science courses part time.Balamurugan Balakreshnan is a principal cloud solution architect at Microsoft Data/AI Architect and Data Science. He has provided leadership on digital transformations with AI and cloud-based digital solutions. He has also provided leadership in terms of ML, the IoT, big data, and advanced analytical solutions.Megan Masanz is a principal cloud solution architect at Microsoft focused on data, AI, and data science, passionately enabling organizations to address business challenges through the establishment of strategies and road maps for the planning, design, and deployment of Azure Cloud-based solutions. Megan is adept at paving the path to data science via computer science given her masters in computer science with a focus on data science. Table of Contents Table of ContentsIntroducing Azure Machine LearningWorking with Data in AMLSTraining Machine Learning Models in AMLSTuning Your Models with AMLSAzure Automated Machine LearningDeploying ML Models for Real-Time InferencingDeploying ML Models for Batch ScoringResponsible AIProductionizing Your Workload with MLOpsUsing Deep Learning in Azure Machine LearningUsing Distributed Training in AMLS Details ISBN1803239301 Author Megan Masanz Pages 362 Publisher Packt Publishing Limited Year 2023 ISBN-13 9781803239309 Format Paperback Imprint Packt Publishing Limited Subtitle Deploy, fine-tune, and optimize ML models using Microsoft Azure Place of Publication Birmingham Country of Publication United Kingdom Publication Date 2023-01-13 AU Release Date 2023-01-13 NZ Release Date 2023-01-13 UK Release Date 2023-01-13 DEWEY 006.31 Audience General We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:139549441;

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