Automated Machine Learning with Microsoft Azure :: Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML.
A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key Features Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2021.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key Features Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft Azure Improve the accuracy of your ML models through automatic data featurization and model training Increase productivity in your organization by using artificial intelligence to solve common problems Book DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect. What you will learn Understand how to train classification, regression, and forecasting ML algorithms with Azure AutoML Prepare data for Azure AutoML to ensure smooth model training and deployment Adjust AutoML configuration settings to make your models as accurate as possible Determine when to use a batch-scoring solution versus a real-time scoring solution Productionalize your AutoML and discover how to quickly deliver value Create real-time scoring solutions with AutoML and Azure Kubernetes Service Train a large number of AutoML models at once using the AzureML Python SDK Who this book is for Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book. |
| Beschreibung: | 1 online resource (340 p.) |
| ISBN: | 1800561970 9781800561977 |
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| 520 | |a A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key Features Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft Azure Improve the accuracy of your ML models through automatic data featurization and model training Increase productivity in your organization by using artificial intelligence to solve common problems Book DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect. What you will learn Understand how to train classification, regression, and forecasting ML algorithms with Azure AutoML Prepare data for Azure AutoML to ensure smooth model training and deployment Adjust AutoML configuration settings to make your models as accurate as possible Determine when to use a batch-scoring solution versus a real-time scoring solution Productionalize your AutoML and discover how to quickly deliver value Create real-time scoring solutions with AutoML and Azure Kubernetes Service Train a large number of AutoML models at once using the AzureML Python SDK Who this book is for Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book. | ||
| 505 | 0 | |a Table of Contents Introducing AutoML Getting Started with Azure Machine Learning Service Training Your First AutoML Model Building an AutoML Regression Solution Building an AutoML Classification Solution Building an AutoML Forecasting Solution Using the Many Models Solution Accelerator Choosing Real-Time versus Batch Scoring Implementing a Batch Scoring Solution Creating End-to-End AutoML Solutions Implementing a Real-Time Scoring Solution Realizing Business Value with AutoML. | |
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| contents | Table of Contents Introducing AutoML Getting Started with Azure Machine Learning Service Training Your First AutoML Model Building an AutoML Regression Solution Building an AutoML Classification Solution Building an AutoML Forecasting Solution Using the Many Models Solution Accelerator Choosing Real-Time versus Batch Scoring Implementing a Batch Scoring Solution Creating End-to-End AutoML Solutions Implementing a Real-Time Scoring Solution Realizing Business Value with AutoML. |
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| spelling | Sawyers, Dennis Michael. Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. Birmingham : Packt Publishing, Limited, 2021. 1 online resource (340 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key Features Create, deploy, productionalize, and scale automated machine learning solutions on Microsoft Azure Improve the accuracy of your ML models through automatic data featurization and model training Increase productivity in your organization by using artificial intelligence to solve common problems Book DescriptionAutomated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect. What you will learn Understand how to train classification, regression, and forecasting ML algorithms with Azure AutoML Prepare data for Azure AutoML to ensure smooth model training and deployment Adjust AutoML configuration settings to make your models as accurate as possible Determine when to use a batch-scoring solution versus a real-time scoring solution Productionalize your AutoML and discover how to quickly deliver value Create real-time scoring solutions with AutoML and Azure Kubernetes Service Train a large number of AutoML models at once using the AzureML Python SDK Who this book is for Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book. Table of Contents Introducing AutoML Getting Started with Azure Machine Learning Service Training Your First AutoML Model Building an AutoML Regression Solution Building an AutoML Classification Solution Building an AutoML Forecasting Solution Using the Many Models Solution Accelerator Choosing Real-Time versus Batch Scoring Implementing a Batch Scoring Solution Creating End-to-End AutoML Solutions Implementing a Real-Time Scoring Solution Realizing Business Value with AutoML. Computer science. http://id.loc.gov/authorities/subjects/sh89003285 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Electronic Data Processing https://id.nlm.nih.gov/mesh/D001330 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Informatique. Intelligence artificielle. artificial intelligence. aat COMPUTERS Data Modeling & Design. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Machine Theory. bisacsh Computer science fast has work: Automated Machine Learning with Microsoft Azure (Text) https://id.oclc.org/worldcat/entity/E39PCY3VfqTTX6mX4dxcK9FD7b https://id.oclc.org/worldcat/ontology/hasWork Print version: Sawyers, Dennis Michael Automated Machine Learning with Microsoft Azure Birmingham : Packt Publishing, Limited,c2021 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2901977 Volltext |
| spellingShingle | Sawyers, Dennis Michael Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. Table of Contents Introducing AutoML Getting Started with Azure Machine Learning Service Training Your First AutoML Model Building an AutoML Regression Solution Building an AutoML Classification Solution Building an AutoML Forecasting Solution Using the Many Models Solution Accelerator Choosing Real-Time versus Batch Scoring Implementing a Batch Scoring Solution Creating End-to-End AutoML Solutions Implementing a Real-Time Scoring Solution Realizing Business Value with AutoML. Computer science. http://id.loc.gov/authorities/subjects/sh89003285 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Electronic Data Processing https://id.nlm.nih.gov/mesh/D001330 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Informatique. Intelligence artificielle. artificial intelligence. aat COMPUTERS Data Modeling & Design. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Machine Theory. bisacsh Computer science fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh89003285 http://id.loc.gov/authorities/subjects/sh85008180 https://id.nlm.nih.gov/mesh/D001330 https://id.nlm.nih.gov/mesh/D001185 |
| title | Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. |
| title_auth | Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. |
| title_exact_search | Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. |
| title_full | Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. |
| title_fullStr | Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. |
| title_full_unstemmed | Automated Machine Learning with Microsoft Azure : Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. |
| title_short | Automated Machine Learning with Microsoft Azure : |
| title_sort | automated machine learning with microsoft azure build highly accurate and scalable end to end ai solutions with azure automl |
| title_sub | Build Highly Accurate and Scalable End-To-end AI Solutions with Azure AutoML. |
| topic | Computer science. http://id.loc.gov/authorities/subjects/sh89003285 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Electronic Data Processing https://id.nlm.nih.gov/mesh/D001330 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Informatique. Intelligence artificielle. artificial intelligence. aat COMPUTERS Data Modeling & Design. bisacsh COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Machine Theory. bisacsh Computer science fast |
| topic_facet | Computer science. Artificial intelligence. Electronic Data Processing Artificial Intelligence Informatique. Intelligence artificielle. artificial intelligence. COMPUTERS Data Modeling & Design. COMPUTERS Intelligence (AI) & Semantics. COMPUTERS Machine Theory. Computer science |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2901977 |
| work_keys_str_mv | AT sawyersdennismichael automatedmachinelearningwithmicrosoftazurebuildhighlyaccurateandscalableendtoendaisolutionswithazureautoml |