Deploying Machine Learning Applications

In the previous chapters, we learned how to create an application that can prepare data (Chapter 2, Setting Up the Development Environment) for either a supervised (Chapter 3, Supervised Learning) or unsupervised (Chapter 4, Unsupervised Learning) ML algorithm. We also learned how to evaluate and test the output of these algorithms with the added complication that we have incomplete knowledge about the algorithm's inner state and workings, and must therefore treat it as a black box. In Chapter 5, Using Pre-Trained Models, we looked at model persistence and how Go applications can leverage models written in other languages. Together, the skills you have learned so far constitute the fundamentals required to successfully prototype ML applications. In this chapter, we will look at how to prepare your prototype for commercial readiness...