Book Image

Managing Data Science

By : Kirill Dubovikov
Book Image

Managing Data Science

By: Kirill Dubovikov

Overview of this book

Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis.
Table of Contents (18 chapters)
Free Chapter
1
Section 1: What is Data Science?
5
Section 2: Building and Sustaining a Team
9
Section 3: Managing Various Data Science Projects
14
Section 4: Creating a Development Infrastructure

Testing Your Models

Coming up with a perfect machine learning model is not simple if you do not use a good testing methodology. This seemingly perfect model will fail the moment you deploy it. Testing the model's performance is not an easy task, but it is an essential part of every data science project. Without proper testing, you can't be sure whether your models will work as expected, and you can't choose the best approach to solve the task at hand.

This chapter will explore various approaches for model testing and look at different types of metrics, using mathematical functions that evaluate the quality of predictions. We will also go through a set of methods for testing classifier models.

In this chapter, we will cover the following topics:

  • Offline model testing
  • Online model testing