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Managing Data Science

You're reading from   Managing Data Science Effective strategies to manage data science projects and build a sustainable team

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781838826321
Length 290 pages
Edition 1st Edition
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Author (1):
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Kirill Dubovikov Kirill Dubovikov
Author Profile Icon Kirill Dubovikov
Kirill Dubovikov
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Table of Contents (18) Chapters Close

1. Section 1: What is Data Science? FREE CHAPTER
2. What You Can Do with Data Science 3. Testing Your Models 4. Understanding AI 5. Section 2: Building and Sustaining a Team
6. An Ideal Data Science Team 7. Conducting Data Science Interviews 8. Building Your Data Science Team 9. Section 3: Managing Various Data Science Projects
10. Managing Innovation 11. Managing Data Science Projects 12. Common Pitfalls of Data Science Projects 13. Creating Products and Improving Reusability 14. Section 4: Creating a Development Infrastructure
15. Implementing ModelOps 16. Building Your Technology Stack 17. Conclusion 18. Other Books You May Enjoy

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...

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