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

Improving your many models results

Now that you have adapted all three of the notebooks to run your own code, you should be feeling pretty confident in your ability to use the MMSA. Still, it's pretty easy to get stuck. Many models is a complicated framework and small errors in your data can lead to errors.

Additionally, sometimes it's really hard to know what your data will look like when you are dealing with thousands of files you wish to train. Here is some good advice to follow in order to ensure you do not come to an impasse when using your own data with the MMSA:

  • Before using the accelerator, always try creating a single model first with your entire dataset. Check the performance of your model. Only use the MMSA if the single model's performance is subpar compared to your expectations or in a situation where obtaining the best accuracy is mission-critical for your project. Sometimes, the trade-off between complexity and performance isn't worth...