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

Automating an end-to-end training solution

Like any other ML model, once an AutoML model is deployed and runs for a few months, it can benefit from being retrained. There are many reasons for this, in order of importance:

  • ML models break if the pattern between your input data and target column changes. This often happens due to extraneous factors such as changes in consumer behavior. When the pattern breaks, you need to retrain your model to retain performance.
  • ML models perform better the more relevant data you feed them. Therefore, as your data grows, you should periodically retrain models.
  • Retraining models on a consistent basis means that they're less likely to break if patterns change slowly over time. Consequently, it's best practice to retrain as data is acquired.

In this section, you are going to put your skills to the test. You will be given a set of instructions similar to when you created an end-to-end scoring solution. However, this time...