Item #:
216441164
Experimentation for Engineers: From A/B Testing to Bayesian Optimization
Item #:
216441164
XPF 10391
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Experimentation for Engineers delivers a toolbox of processes for optimizing software systems.
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What Stands Out
Comprehensive Coverage
Explores a wide range of experimentation techniques, empowering engineers with both A/B testing and Bayesian optimization methods to enhance their experimental insights.
Practical Applications
Equips readers with real-world applications and case studies, demonstrating how various experimentation strategies solve engineering challenges effectively and efficiently.
Accessible Learning
Presents complex concepts in a digestible manner, making it suitable for both beginners and experienced engineers looking to refine their experimentation skills.
Product Details
- Optimise the performance of your systems with practical experiments used by engineers in the world's most competitive industries.Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox of techniques for evaluating new features and fine-tuning parameters. You will start with a deep dive into methods like A/B testing and then graduate to advanced techniques used to measure performance in industries such as finance and social media.You will learn how to:Design, run, and analyse an A/B testBreak the feedback loops caused by periodic retraining of ML modelsIncrease experimentation rate with multi-armed banditsTune multiple parameters experimentally with Bayesian optimisationClearly define business metrics used for decision-makingIdentify and avoid the common pitfalls of experimentationBy the time you're done, you will be able to seamlessly deploy experiments in production, whilst avoiding common pitfalls.About the technologyDoes my software really work? Did my changes make things better or worse? Should I trade features for performance? Experimentation is the only way to answer questions like these. This unique book reveals sophisticated experimentation practices developed and proven in the world's most competitive industries and will help you enhance machine learning systems, software applications, and quantitative trading solutions.
| Publisher | Manning Publications |
| Publication date | 7 Mar. 2023 |
| Edition | 1st |
| Language | English |
| Print length | 248 pages |
| ISBN-10 | 1617298158 |
| ISBN-13 | 978-1617298158 |
| Dimensions | 18.73 x 0.76 x 23.5 cm |
Who Should Buy?
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Data Scientists
Ideal for data scientists looking to enhance their skills in experimental design and optimization techniques.
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Software Engineers
Software engineers can gain insights into A/B testing methodologies to improve product features and user experiences.
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Business Analysts
Business analysts will benefit from understanding how to use experimentation for data-driven decision-making and strategy.
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Beginners In Statistics
Individuals with no statistical background may find the concepts and methodologies challenging without prior knowledge.
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English edition David Sweet Format: Paperback Editorial Review
Experimentation For Engineers: From A/B Testing To Bayesian Optimization is an insightful resource published by Manning Publications on March 7, 2023. This 1st edition book spans 248 pages and provides a thorough exploration of experimentation techniques in engineering, making it a valuable addition to any engineer's library. Readers have praised its clarity and practical approaches, which are crucial for implementing effective A/B testing and optimization strategies. The dimensions of the book are 18.73 x 0.76 x 23.5 cm, making it easy to handle and read, perfect for both novice and experienced engineers looking to enhance their skills in this field.
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Pros
- Clear and practical approaches to experimentation
- Valuable insights for engineers and data scientists
- Suitable for both novice and experienced readers
- Published by a reputable publisher
- Well-structured content
Cons
- Some readers may find it a bit technical
Product Price History
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Features & Benefits
- Master A/B testing and advanced experimentation strategies.
- Break feedback loops from periodic retraining of ML models.
- Increase experimentation rates using multi-armed bandits.
- Tune parameters with Bayesian optimization for best results.
- Avoid common pitfalls in experimentation to protect business metrics.
- Includes purchase of a print book plus free eBook in multiple formats.
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