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

Improving reusability

Improving reusability is a custom project development setting where you develop and reuse internal components to build better solutions faster. Look at what parts of your work are repeated in all of your projects. For many companies, it's the model deployment and serving. For others, it is building the dashboards on top of the model.

Use open source projects as a starting point. In many fields, the best tools are provided by commercial companies. Thankfully, the data science community is a very open-minded group, and the best tools you can find are open source. Of course, there are great commercial products too, but you can build production-grade systems with state-of-the-art models using open solutions. They will give you a very solid foundation to build upon.

See if you can use those tools to decrease the total amount of time your team spends on similar...