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Automated Machine Learning with Microsoft Azure

You're reading from   Automated Machine Learning with Microsoft Azure Build highly accurate and scalable end-to-end AI solutions with Azure AutoML

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Product type Paperback
Published in Apr 2021
Publisher Packt
ISBN-13 9781800565319
Length 340 pages
Edition 1st Edition
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Authors (2):
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Dennis Michael Sawyers Dennis Michael Sawyers
Author Profile Icon Dennis Michael Sawyers
Dennis Michael Sawyers
Dennis Sawyers Dennis Sawyers
Author Profile Icon Dennis Sawyers
Dennis Sawyers
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: AutoML Explained – Why, What, and How
2. Chapter 1: Introducing AutoML FREE CHAPTER 3. Chapter 2: Getting Started with Azure Machine Learning Service 4. Chapter 3: Training Your First AutoML Model 5. Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
6. Chapter 4: Building an AutoML Regression Solution 7. Chapter 5: Building an AutoML Classification Solution 8. Chapter 6: Building an AutoML Forecasting Solution 9. Chapter 7: Using the Many Models Solution Accelerator 10. Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions
11. Chapter 8: Choosing Real-Time versus Batch Scoring 12. Chapter 9: Implementing a Batch Scoring Solution 13. Chapter 10: Creating End-to-End AutoML Solutions 14. Chapter 11: Implementing a Real-Time Scoring Solution 15. Chapter 12: Realizing Business Value with AutoML 16. Other Books You May Enjoy

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "You have another helper function here, get_forecasting_output."

A block of code is set as follows:

from azureml.core import Workspace, Dataset, Datastore
from azureml.core import Experiment
from azureml.core.compute import ComputeTarget
from azureml.train.automl import AutoMLConfig
from azureml.train.automl.run import AutoMLRun
from azureml.widgets import RunDetails

Any command-line input or output is written as follows:

from azureml.pipeline.core import PipelineRun
experiment = Experiment(ws, 'your-experiment_name')
pipeline_run = PipelineRun(experiment, 'your-pipeline-run-id')

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Go to Experiments under Assets in Azure Machine Learning Studio, click your experiment name, select your run ID, click the Models tab, select the highest-performing algorithm, and click the Metrics tab."

Tips or important notes

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