Book Image

Automated Machine Learning with Microsoft Azure

By : Dennis Michael Sawyers
Book Image

Automated Machine Learning with Microsoft Azure

By: Dennis Michael 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)
Section 1: AutoML Explained – Why, What, and How
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Scoring new data for many models

Scoring new data with the MMSA is a fairly straightforward task. Once you have your models trained, simply navigate to the correct notebook, change your variables to match your training notebook, and click the run button. As there are very few settings to alter compared to the training notebook, it's even easier to use with your own code.

In this section, like the others, first you will run the out-of-the-box scoring notebook with OJ Sales. Then, you will create a new notebook to score the sample data.

Scoring OJ sales data with the MMSA

To score OJ Sales data with the multiple models you've trained, follow these steps:

  1. From the solution-accelerator-many-models folder, open the Automated_ML folder.
  2. From the Automated_ML folder, open the 03_AutoML_Forecasting_Pipeline folder.
  3. Open 03_AutoML_Forecasting_Pipeline.ipynb.
  4. Run all of the cells in section 1.0. These cells set up your AMLS workspace, compute cluster...