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 scoring solution

Ultimately, the end goal of any AutoML project is to create an automated scoring solution. Data gets pulled in from a source, scored automatically using the model you trained, and the results get stored in a location of your choice. By combining everything you've learned in the previous three sections, you can accomplish this task easily.

You will begin this section by opening up AMLS, creating a new dataset, and slightly altering your existing Iris-Scoring-Pipeline. Then, after republishing your pipeline with a new name, you will combine it with the Copy data activity you created to load data into Azure.

Next, you will create another Copy Data activity to transfer your results from Azure to your PC and schedule the job to run once a week on Mondays. This is a very common pattern in ML, and it's one you can accomplish without any code at all using ADF.

Editing an ML pipeline to score new data

First, you need to create...