Book Image

Mastering Java for Data Science

By : Alexey Grigorev
Book Image

Mastering Java for Data Science

By: Alexey Grigorev

Overview of this book

Java is the most popular programming language, according to the TIOBE index, and it is a typical choice for running production systems in many companies, both in the startup world and among large enterprises. Not surprisingly, it is also a common choice for creating data science applications: it is fast and has a great set of data processing tools, both built-in and external. What is more, choosing Java for data science allows you to easily integrate solutions with existing software, and bring data science into production with less effort. This book will teach you how to create data science applications with Java. First, we will revise the most important things when starting a data science application, and then brush up the basics of Java and machine learning before diving into more advanced topics. We start by going over the existing libraries for data processing and libraries with machine learning algorithms. After that, we cover topics such as classification and regression, dimensionality reduction and clustering, information retrieval and natural language processing, and deep learning and big data. Finally, we finish the book by talking about the ways to deploy the model and evaluate it in production settings.
Table of Contents (17 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Chapter 10. Deploying Data Science Models

So far we have covered a lot of data science models, we talked about many supervised and unsupervised learning methods, including deep learning and XGBoost, and discussed how we can apply these models to text and graph data.

In terms of the CRISP-DM methodology, we mostly covered the modeling part so far. But there are other important parts we have not yet discussed: evaluation and deployment. These steps are quite important in the application lifecycle, because the models we create should be useful for the business and bring value, and the only way to achieve that is integrate them into the application (the deployment part) and make sure they indeed are useful (the evaluation part).

In this last chapter of the book we will cover exactly these missing parts--we will see how we can deploy data science models so they can be used by other services of the application. In addition to that, we will also see how to perform an online evaluation of already...