Chapter 7. Production Systems

Up to this point in the book, we have focused our discussion on implementing machine learning algorithms for security in isolated lab environments. After you have proven that the algorithm works, the next step will likely be to get the software ready for production. Deploying machine learning systems in production comes with an entirely different set of challenges and concerns that you might not have had to deal with during the experimentation and development phases. What does it take to engineer truly scalable machine learning systems? How do we manage the efficacy, reliability, and relevance of web-scale security services in dynamic environments where change is constant? This chapter is dedicated to security and data at scale, and we will address these questions and more.

Let’s begin by concretely defining what it means for such systems to be production ready, deployable, and scalable.

Defining Machine Learning System Maturity and Scalability

Instead of floating around abstract terms for describing the quality of code in production, it will benefit the discussion to detail some characteristics that mature and scalable machine learning systems should have. The following list of features describes an ideal machine learning system, regardless of whether it is related to security; the items highlighted in bold are especially important for security machine learning systems. The list also serves as an outline for the remainder of this chapter, so if ...

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