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)
1
Section 1: AutoML Explained – Why, What, and How
5
Section 2: AutoML for Regression, Classification, and Forecasting – A Step-by-Step Guide
10
Section 3: AutoML in Production – Automating Real-Time and Batch Scoring Solutions

Improving performance on your AKS cluster

Sometimes you will deploy an endpoint on AKS and it doesn't perform how you'd like. Maybe it times out, maybe it's too slow, maybe an endpoint that was previously working fine suddenly gets a lot more traffic that it cannot handle. These situations happen, and you must be prepared to face them.

Thankfully, AKS deployments have a lot of additional configurations that you can take advantage of to solve these problems. This section covers some of the more common situations as follows:

  • Depending on how complex your model is, how many data points you are trying to score, and the size of your VMs, AKS models can sometimes take a while to score or even timeout. In this situation, there are many things you can do.

    First, you can try increasing the size of your VM, selecting one with more RAM. Next, you can add an additional setting to your deployment configuration, scoring_timeout_ms. This setting defaults to 60000 milliseconds...