Why write your own?

It seems like all of the major software companies are pitching machine learning services such as Google Analytics, Amazon Machine Learning Services, IBM Watson, Microsoft Cortana Analytics, to name a few. In addition, major software companies often try to sell products that have a machine learning component, such as Microsoft SQL Server Analysis Service, Oracle Database Add-In, IBM SPSS, or SAS JMP. I have not included some common analytical software packages such as PowerBI or Tableau because they are more data aggregation and report writing applications. Although they do analytics, they do not have a machine learning component (not yet at least).

With all these options, why would you want to learn how to implement machine learning inside your applications, or in effect, write some code that you can purchase elsewhere? It is the classic build versus buy decision that every department or company has to make. You might want to build because:

• You really understand what you are doing and you can be a much more informed consumer and critic of any given machine learning package. In effect, you are building your internal skill set that your company will most likely prize. Another way to look at it, companies are not one tool away from purchasing competitive advantage because if they were, their competitors could also buy the same tool and cancel any advantage. However, companies can be one hire away or more likely one team away to truly have the ability to differentiate themselves in their market.
• You can get better performance by executing locally, which is especially important for real-time machine learning and can be implemented in disconnected or slow connection scenarios. This becomes particularly important when we start implementing machine learning with Internet of Things (IoT) devices in scenarios where the device has a lot more RAM than network bandwidth. Consider the Raspberry Pi running Windows 10 on a pipeline. Network communication might be spotty, but the machine has plenty of power to implement ML models.
• You are not beholden to any one vendor or company, for example, every time you implement an application with a specific vendor and are not thinking about how to move away from the vendor, you make yourself more dependent on the vendor and their inevitable recurring licensing costs. The next time you are talking to the CTO of a shop that has a lot of Oracle, ask him/her if they regret any decision to implement any of their business logic in Oracle databases. The answer will not surprise you. A majority of this book's code is written in F#—an open source language that runs great on Windows, Linux, and OS X.
• You can be much more agile and have much more flexibility in what you implement. For example, we will often re-train our models on the fly and when you write your own code, it is fairly easy to do this. If you use a third-party service, they may not even have API hooks to do model training and evaluation, so near-time model changes are impossible.

Once you decide to go native, you have a choice of rolling your own code or using some of the open source assemblies out there. This book will introduce both the techniques to you, highlight some of the pros and cons of each technique, and let you decide how you want to implement them. For example, you can easily write your own basic classifier that is very effective in production but certain models, such as a neural network, will take a considerable amount of time and energy and probably will not give you the results that the open source libraries do. As a final note, since the libraries that we will look at are open source, you are free to customize pieces of it—the owners might even accept your changes. However, we will not be customizing these libraries in this book.