Chapter 1. First Steps to Scalability

Welcome to this book on scalable machine learning with Python.

In this chapter, we will discuss how to learn effectively from big data with Python and how it can be possible using your single machine or a cluster of other machines, which you can get, for instance, from Amazon Web Services (AWS) or the Google Cloud Platform.

In the book, we will be using Python's implementation of machine learning algorithms that are scalable. This means that they can work with a large amount of data and do not crash because of memory constraints. They also take a reasonable amount of time, which is something manageable for a data science prototype and also deployment in production. Chapters are organized around solutions (such as streaming data), algorithms (such as neural networks or ensemble of trees), and frameworks (such as Hadoop or Spark). We will also provide you with some basic reminders about the machine learning algorithms and explain how to make them scalable and suitable to problems with massive datasets.

Given such premises as a start, you'll need to learn the basics (so as to figure out the perspective under which this book has been written) and set up all your basic tools to start reading the chapters immediately.

In this chapter, we will introduce you to the following topics:

• What scalability actually means
• What bottlenecks you should pay attention to when dealing with data
• What kind of problems this book will help you solve
• How to use Python to analyze datasets at scale effectively
• How to set up your machine quickly to execute the examples presented in this book

Let's start this journey together around scalable solutions with Python!