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DevOps Culture and Practice with OpenShift

You're reading from  DevOps Culture and Practice with OpenShift

Product type Book
Published in Aug 2021
Publisher Packt
ISBN-13 9781800202368
Pages 812 pages
Edition 1st Edition
Languages
Concepts
Authors (5):
Tim Beattie Tim Beattie
Profile icon Tim Beattie
Mike Hepburn Mike Hepburn
Profile icon Mike Hepburn
Noel O'Connor Noel O'Connor
Profile icon Noel O'Connor
Donal Spring Donal Spring
Profile icon Donal Spring
Ilaria Doria Ilaria Doria
Profile icon Ilaria Doria
View More author details
Toc

Table of Contents (30) Chapters close

Preface Acknowledgements Section 1: Practices Make Perfect
1. Introduction — Start with Why 2. Introducing DevOps and Some Tools 3. The Journey Ahead Section 2: Establishing the Foundation
4. Open Culture 5. Open Environment and Open Leadership 6. Open Technical Practices – Beginnings, Starting Right 7. Open Technical Practices — The Midpoint Section 3: Discover It
8. Discovering the Why and Who 9. Discovering the How 10. Setting Outcomes Section 4: Prioritize It
11. The Options Pivot Section 5: Deliver It
12. Doing Delivery 13. Measure and Learn Section 6: Build It, Run It, Own It
14. Build It 15. Run It 16. Own It Section 7: Improve It, Sustain It
17. Improve It 18. Sustain It Index
Appendix A – OpenShift Sizing Requirements for Exercises 1. Appendix B – Additional Learning Resources

Design of Experiments

All our ideas for new products, services, features, and indeed any changes we can introduce to make things better (more growth, increased revenue, enhanced experience, and so on) start off as a hypothesis or an assumption. In a traditional approach to planning, a team may place bets on which experiment to run based on some form of return on investment-style analysis, while making further assumptions in the process.

Design of Experiments is an alternative to this approach, in which we try to validate as many of the important ideas/hypotheses/assumptions we are making as early as possible. Some of those objects of the experiments we may want to keep open until we get some real-world proof, which can be done through some of the advanced deployment capability (such as A/B Testing) that we'll explore later in this chapter.

Design of Experiments is a practice we use to turn ideas, hypotheses, or assumptions into concrete, well-defined sets of experiments...

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