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Automated Machine Learning with Microsoft Azure

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers , Dennis Sawyers
4.9 (18)
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Automated Machine Learning with Microsoft Azure

Automated Machine Learning with Microsoft Azure

4.9 (18)
By: Dennis Michael Sawyers , Dennis Sawyers

Overview of this book

Automated 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.
Table of Contents (17 chapters)
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1
Section 1: AutoML Explained – Why, What, and How
5
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
10
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Technical requirements

Within this chapter, you will log in to your Azure Machine Learning studio (AMLS), open up a Jupyter notebook on a compute instance, and install the MMSA from its location on GitHub. You will then run all three pieces of the MMSA sequentially, prepping the data, training the models remotely, and forecasting the data. As such, you need to have an Azure account, a compute instance for writing Python code, and a compute cluster for remote training. The full list of requirements is as follows:

  • Access to the internet.
  • A web browser, preferably Google Chrome or Microsoft Edge Chromium.
  • A Microsoft Azure account.
  • You should have created an AMLS workspace.
  • You should have created a compute instance in Chapter 2, Getting Started with Azure Machine Learning Service.
  • You should have created the compute cluster in Chapter 2, Getting Started with Azure Machine Learning Service.
  • You should understand how to navigate to the Jupyter environment...
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