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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By : Perrier
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Effective Amazon Machine Learning

Effective Amazon Machine Learning

By: Perrier

Overview of this book

Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets.
Table of Contents (10 chapters)
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Making real-time predictions


With batch predictions, you submit all the samples you want the model to predict at once to Amazon ML by creating a datasource. With real-time predictions, also called streaming or online predictions, the idea is to send one sample at a time to an API endpoint, a URL, via HTTP queries, and receive back predictions and information for each one of the samples.

Setting up real-time predictions on a model consists of knowing the prediction API endpoint URL and writing a script that can read your data, send each new sample to that API URL, and retrieve the predicted class or value. We will present a Python-based example in the following section.

Amazon ML also offers a way to make predictions on data you create on the fly on the prediction page. We can input the profile of a would-be passenger on the Titanic and see whether that profile would have survived or not. It is a great way to explore the influence of the dataset variables on the outcome.

Before setting up API...

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