Some motivating examples may make things clearer and more memorable for you.
Let's take two simple examples:
• Being able to predict the click-through rate (CTR) can help you earn quite a lot these days when Internet advertising is so widespread, diffused, and eating large shares of traditional media communication
• Being able to propose the right information to your customers, when they are searching the products and services offered by your site, could really enhance your chances to sell if you can guess what to put at the top of their results
In both cases, we have quite large datasets as they are produced by users' interactions on the Internet.
Depending on the business that we have in mind (we can imagine some big players here), we are clearly talking of millions of data points per day in both our examples. In the advertising case, data is certainly tall, being a continuous stream of information as the most recent data, more representative of markets and consumers, replaces the older one. In the search engine case, data is wide, being enriched by the feature provided by the results you offered to your customers: for instance, if you are in the travels business, you will have quite a lot of features about hotels, locations, and services offered.
Chapter 1 Clearly, scalability is an issue for both these problems:
• You have to learn from data that is growing every day and you have to learn fast because as you are learning, new data keeps arriving. Yet, you have to deal with data that clearly cannot fit in memory because the matrix is too tall or too large.
• You frequently need to update your machine learning model in order to accommodate new data. You need an algorithm that can process the information in a timely manner. O(n2) or O(n3) complexities could be impossible for you to handle because of the data quantity; you need some algorithm that can work with lower complexity (such as O(n)) or by dividing the data so that n will be much, much smaller.
• You have to be able to predict fast because the predictions have to be delivered only to new customers. Again, the complexity of your algorithm does matter.
The scalability problem can be solved in one or multiple ways:
• Scaling up by reducing the dimensionality of the problem; for instance, in the case of the search engine, by effectively selecting the relevant features to be used
• Scaling up using the right algorithm; for instance, in the case of advertising data, there are appropriate algorithms to learn effectively from streams
• Scaling out the learning process by leveraging multiple machines
• Scaling up the deployment process using multiprocessing and vectorization on a single server effectively
In this book, we will point out for you what kind of practical problems can be solved by each one of the solutions or algorithms proposed. It will become automatic for you to connect a particular constraint in time and execution (CPU, memory, or I/O) to the most suitable solution among the ones that we propose.
Introducing Python
As our treatise will depend on Python—our open source language of choice for this book—we have to stop for a brief moment and present the language before clarifying how Python can easily help you scale up and out with your massive data problem.
First Steps to Scalability
Created in 1991 as a general-purpose, interpreted, object-oriented language, Python has slowly and steadily conquered the scientific community and grown into a mature ecosystem of specialized packages for data processing and analysis. It allows you to have uncountable and fast experimentations, easy theory developments, and prompt deployments of scientific applications.
As a machine learning practitioner, you will find using Python interesting for various reasons:
• It offers a large, mature system of packages for data analysis and machine learning. It guarantees that you will get all that you may need in the course of a data analysis, and sometimes even more.
• It is very versatile. No matter what your programming background or style is (object-oriented or procedural), you will enjoy programming with Python.
• If you don't know it yet but you know other languages such as C/C++ or Java well, then it is very simple to learn and use. After you grasp the basics, there's no other better way to learn more than by immediately starting with the coding.
• It is cross-platform; your solutions will work perfectly and smoothly on Windows, Linux, and macOS systems. You won't have to worry about portability.
• Although interpreted, it is undoubtedly fast compared to other mainstream data analysis languages such as R and MATLAB (though it is not comparable to C, Java, and the newly emerged Julia language).
• It can work with in-memory big data because of its minimal memory
footprint and excellent memory management. The memory garbage collector will often save the day when you load, transform, dice, slice, save, or discard data using the various iterations and reiterations of data wrangling.
If you are not already an expert (and actually we require some basic knowledge of Python in order to be able to make the most out of this book), you can read everything about the language and find the basic installations files directly from the Python foundations at https://www.python.org/.
Chapter 1