Book Image

Effective Amazon Machine Learning

By : Alexis Perrier
Book Image

Effective Amazon Machine Learning

By: Alexis 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 (17 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Summary


In this chapter, we explored the final step in the Amazon ML workflow, the predictions. Amazon ML offers several ways to apply your models to new datasets in order to make predictions. Batch mode involves submitting all the new data at once to the model and returning the actual predictions in a csv file on S3. Real-time predictions, on the other hand, are based on sending samples one by one to an API and getting prediction results in return. We looked at how to create an API on the Amazon ML platform. We also started using the command line and the Python SDK to interact with the Amazon ML service -- something we will explore in more depth in Chapter 7, Command Line and SDK.

As explained in the previous chapters, the Amazon ML service is built around the Stochastic Gradient Descent (SGD) algorithm. This algorithm has been around for many years and is used in many different domains and applications, from signal processing and adaptive filtering to predictive analysis or deep learning...