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Megan Masanz
Azure Machine Learning Engineering
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1012664Azure Machine Learning Engineeringhttps://www.gandhi.com.mx/azure-machine-learning-engineering/phttps://gandhi.vtexassets.com/arquivos/ids/686238/6d92ae1e-ccbd-4c5b-b667-21133338e328.jpg?v=638335795692930000621690MXNPackt PublishingInStock/Ebooks/<p><b>Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service</b></p><h4>Key Features</h4><ul><li>Automate complete machine learning solutions using Microsoft Azure</li><li>Understand how to productionize machine learning models</li><li>Get to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learning</li></ul><h4>Book Description</h4>Data 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.<h4>What you will learn</h4><ul><li>Train ML models in the Azure Machine Learning service</li><li>Build end-to-end ML pipelines</li><li>Host ML models on real-time scoring endpoints</li><li>Mitigate bias in ML models</li><li>Get the hang of using an MLOps framework to productionize models</li><li>Simplify ML model explainability using the Azure Machine Learning service and Azure Interpret</li></ul><h4>Who this book is for</h4><p>Machine 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.</p>...1007837Azure Machine Learning Engineering621690https://www.gandhi.com.mx/azure-machine-learning-engineering/phttps://gandhi.vtexassets.com/arquivos/ids/686238/6d92ae1e-ccbd-4c5b-b667-21133338e328.jpg?v=638335795692930000InStockMXN99999DIEbook20239781803241685_W3siaWQiOiIzOWJiNjNlNy00MDY4LTRlN2ItOWYxNC0xZDc4MTJiZmU3NTMiLCJsaXN0UHJpY2UiOjY5MCwiZGlzY291bnQiOjY5LCJzZWxsaW5nUHJpY2UiOjYyMSwiaW5jbHVkZXNUYXgiOnRydWUsInByaWNlVHlwZSI6Ildob2xlc2FsZSIsImN1cnJlbmN5IjoiTVhOIiwiZnJvbSI6IjIwMjQtMDQtMDhUMTY6MDA6MDBaIiwicmVnaW9uIjoiTVgiLCJpc1ByZW9yZGVyIjpmYWxzZX1d9781803241685_<p><strong>Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service.</strong></p><h4>Key Features</h4><ul><li>Automate full end-to-end machine learning solutions using Microsoft Azure</li><li>Understand how to productionalize Machine Learning Models.</li><li>Learn Monitoring, MLOps, Deep Learning, Distributed Training and Reinforcement Learning</li></ul><h4>Book Description</h4><p>Azure Machine Learning Service to train machine learning models and productionize workloads Data Scientists often have trouble productionizing workloads and this will teach them how to do it. Data Scientists working with Azure will be able to put their knowledge to work with this practical guide to Machine Learning Engineering. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no time. Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will begin by training a model in Azure Machine Learning Service followed by step-by-step instructions in productionizing your model. Youll learn how to train, register, and productionize machine learning models using Azure Machine Learning Service. Youll learn how to score models in real-time and in batch, how to explain models to earn business trust, how to mitigate model bias and develop solutions using an MLOps framework.</p><p>By the end of this book, you will be able to fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service.</p><h4>What you will learn</h4><ul><li>Train Machine Learning Models in Azure Machine Learning Service</li><li>Build end-to-end Machine Learning Pipelines</li><li>Host Machine Learning Models on Real-Time Scoring Endpoints</li><li>Explain Machine Learning Models and Mitigate Bias</li><li>Use and MLOps Framework to Productionize Models</li></ul><h4>Who This Book Is For</h4><p>Machine Learning Engineers and Data Scientists who want to move to ML Engineering roles will find this book useful. Familiarity with Azure Ecosystem is a plus.</p><h4>Table of Contents</h4><ol><li>Introducing Azure Machine Learning Service</li><li>Working with Data in AMLS</li><li>Training Machine Learning Models in AMLS</li><li>Tuning your models with AMLS</li><li>Azure Automated Machine Learning</li><li>Deploying ML Models for Real-Time Inferencing</li><li>Deploying ML Models for Batch Scoring</li><li>Responsible AI</li><li>Productionizing your Workload with MLOps</li><li>Using Deep Learning in AMLS</li><li>Using Distributed Training in AMLS</li></ol>...(*_*)9781803241685_<p><b>Fully build and productionize end-to-end machine learning solutions using Azure Machine Learning Service</b></p><h4>Key Features</h4><ul><li>Automate complete machine learning solutions using Microsoft Azure</li><li>Understand how to productionize machine learning models</li><li>Get to grips with monitoring, MLOps, deep learning, distributed training, and reinforcement learning</li></ul><h4>Book Description</h4>Data 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.<h4>What you will learn</h4><ul><li>Train ML models in the Azure Machine Learning service</li><li>Build end-to-end ML pipelines</li><li>Host ML models on real-time scoring endpoints</li><li>Mitigate bias in ML models</li><li>Get the hang of using an MLOps framework to productionize models</li><li>Simplify ML model explainability using the Azure Machine Learning service and Azure Interpret</li></ul><h4>Who this book is for</h4><p>Machine 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.</p>...9781803241685_Packt Publishinglibro_electonico_ffeb8ac1-ac06-3f2c-8271-4b0a1c2928ff_9781803241685;9781803241685_9781803241685Megan MasanzInglésMéxicohttps://getbook.kobo.com/koboid-prod-public/packt-epub-4a52156d-8907-4294-81be-2fd4c53a5ed0.epub2023-01-20T00:00:00+00:00Packt Publishing