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Managing Data Science

You're reading from  Managing Data Science

Product type Book
Published in Nov 2019
Publisher Packt
ISBN-13 9781838826321
Pages 290 pages
Edition 1st Edition
Languages
Author (1):
Kirill Dubovikov Kirill Dubovikov
Profile icon Kirill Dubovikov
Toc

Table of Contents (18) Chapters close

1. Section 1: What is Data Science?
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

Case study – building ModelOps for a predictive maintenance system

Oliver is a team leader of a data science project for a large manufacturing company called MannCo, whose plants can be found in multiple cities around the country. Oliver's team developed a predictive maintenance model that can help MannCo to forecast and prevent expensive equipment breakages, which result in costly repairs and long production line outages. The model takes measurements of multiple sensors as input and outputs a package probability that can be used to plan a diagnostics and repair session.

This example contains some technical details. If you are unfamiliar with the technologies mentioned in this case study, you may want to follow links to get a better understanding of the details.

Each piece of this equipment is unique in its own way because it operates under different conditions on...

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