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

Creating an AutoML training pipeline

Sometimes, it's necessary to retrain a model that you trained in AutoML. ML models can degrade over time if the relationship between your data and your target variable changes. This is true for all ML models, not just ones generated by AutoML.

Imagine, for example, that you build an ML model to predict demand for frozen pizza at a supermarket, and then one day, a famous pizza chain sets up shop next door. It's very likely that consumer buying behavior will change, and you will need to retrain the model. This is true for all ML models.

Luckily, AMLS has specialized ML pipeline steps built specifically for retraining models. In this section, we are going to use one of those steps, the AutoML step. The AutoML step lets you retrain models easily whenever you want, either with a push of a button or on a schedule.

Here, you will build a two-step ML pipeline where you will first train a model with an AutoML step and register it with...