Mastering Azure machine learning :: execute large-scale end-to-end machine learning with Azure /
Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services. Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing Ltd.,
2022.
|
| Ausgabe: | Second edition. |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services. Azure 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, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll 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, you'll be able to combine all the steps you've learned by building an MLOps pipeline. |
| Beschreibung: | 1 online resource (624 pages) : illustrations |
| ISBN: | 9781803246796 1803246790 |
Internformat
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| 520 | |a Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services. Azure 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, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll 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, you'll be able to combine all the steps you've learned by building an MLOps pipeline. | ||
| 505 | 0 | |a Table of Contents Understanding the End-to-End Machine Learning Process Choosing the Right Machine Learning Service in Azure Preparing the Azure Machine Learning Workspace Ingesting Data and Managing Datasets Performing Data Analysis and Visualization Feature Engineering and Labeling Advanced Feature Extraction with NLP Azure Machine Learning Pipelines Building ML Models Using Azure Machine Learning Training Deep Neural Networks on Azure Hyperparameter Tuning and Automated Machine Learning Distributed Machine Learning on Azure Building a Recommendation Engine in Azure Model Deployment, Endpoints, and Operations Model Interoperability, Hardware Optimization, and Integrations Bringing Models into Production with MLOps Preparing for a Successful ML Journey. | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Cloud computing. |0 http://id.loc.gov/authorities/subjects/sh2008004883 | |
| 650 | 0 | |a Microsoft Azure (Computing platform) |0 http://id.loc.gov/authorities/subjects/sh2016001752 | |
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| 650 | 6 | |a Infonuagique. | |
| 650 | 6 | |a Microsoft Azure (Plateforme informatique) | |
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| author | Körner, Christoph Alsdorf, Marcel |
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| contents | Table of Contents Understanding the End-to-End Machine Learning Process Choosing the Right Machine Learning Service in Azure Preparing the Azure Machine Learning Workspace Ingesting Data and Managing Datasets Performing Data Analysis and Visualization Feature Engineering and Labeling Advanced Feature Extraction with NLP Azure Machine Learning Pipelines Building ML Models Using Azure Machine Learning Training Deep Neural Networks on Azure Hyperparameter Tuning and Automated Machine Learning Distributed Machine Learning on Azure Building a Recommendation Engine in Azure Model Deployment, Endpoints, and Operations Model Interoperability, Hardware Optimization, and Integrations Bringing Models into Production with MLOps Preparing for a Successful ML Journey. |
| ctrlnum | (OCoLC)1317831602 |
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| discipline | Informatik |
| edition | Second edition. |
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| spelling | Körner, Christoph, author. Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / Christoph Körner, Marcel Alsdorf. Execute large-scale end-to-end machine learning with Azure Second edition. Birmingham, UK : Packt Publishing Ltd., 2022. 1 online resource (624 pages) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Supercharge and automate your deployments to Azure Machine Learning clusters and Azure Kubernetes Service using Azure Machine Learning services. Azure 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, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll 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, you'll be able to combine all the steps you've learned by building an MLOps pipeline. Table of Contents Understanding the End-to-End Machine Learning Process Choosing the Right Machine Learning Service in Azure Preparing the Azure Machine Learning Workspace Ingesting Data and Managing Datasets Performing Data Analysis and Visualization Feature Engineering and Labeling Advanced Feature Extraction with NLP Azure Machine Learning Pipelines Building ML Models Using Azure Machine Learning Training Deep Neural Networks on Azure Hyperparameter Tuning and Automated Machine Learning Distributed Machine Learning on Azure Building a Recommendation Engine in Azure Model Deployment, Endpoints, and Operations Model Interoperability, Hardware Optimization, and Integrations Bringing Models into Production with MLOps Preparing for a Successful ML Journey. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Microsoft Azure (Computing platform) http://id.loc.gov/authorities/subjects/sh2016001752 Apprentissage automatique. Infonuagique. Microsoft Azure (Plateforme informatique) Cloud computing fast Machine learning fast Microsoft Azure (Computing platform) fast Alsdorf, Marcel, author. Print version: 9781803232416 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3274268 Volltext |
| spellingShingle | Körner, Christoph Alsdorf, Marcel Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / Table of Contents Understanding the End-to-End Machine Learning Process Choosing the Right Machine Learning Service in Azure Preparing the Azure Machine Learning Workspace Ingesting Data and Managing Datasets Performing Data Analysis and Visualization Feature Engineering and Labeling Advanced Feature Extraction with NLP Azure Machine Learning Pipelines Building ML Models Using Azure Machine Learning Training Deep Neural Networks on Azure Hyperparameter Tuning and Automated Machine Learning Distributed Machine Learning on Azure Building a Recommendation Engine in Azure Model Deployment, Endpoints, and Operations Model Interoperability, Hardware Optimization, and Integrations Bringing Models into Production with MLOps Preparing for a Successful ML Journey. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Microsoft Azure (Computing platform) http://id.loc.gov/authorities/subjects/sh2016001752 Apprentissage automatique. Infonuagique. Microsoft Azure (Plateforme informatique) Cloud computing fast Machine learning fast Microsoft Azure (Computing platform) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh2008004883 http://id.loc.gov/authorities/subjects/sh2016001752 |
| title | Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / |
| title_alt | Execute large-scale end-to-end machine learning with Azure |
| title_auth | Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / |
| title_exact_search | Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / |
| title_full | Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / Christoph Körner, Marcel Alsdorf. |
| title_fullStr | Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / Christoph Körner, Marcel Alsdorf. |
| title_full_unstemmed | Mastering Azure machine learning : execute large-scale end-to-end machine learning with Azure / Christoph Körner, Marcel Alsdorf. |
| title_short | Mastering Azure machine learning : |
| title_sort | mastering azure machine learning execute large scale end to end machine learning with azure |
| title_sub | execute large-scale end-to-end machine learning with Azure / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Cloud computing. http://id.loc.gov/authorities/subjects/sh2008004883 Microsoft Azure (Computing platform) http://id.loc.gov/authorities/subjects/sh2016001752 Apprentissage automatique. Infonuagique. Microsoft Azure (Plateforme informatique) Cloud computing fast Machine learning fast Microsoft Azure (Computing platform) fast |
| topic_facet | Machine learning. Cloud computing. Microsoft Azure (Computing platform) Apprentissage automatique. Infonuagique. Microsoft Azure (Plateforme informatique) Cloud computing Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3274268 |
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