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185827877
Experimentation for Engineers: From A/B Testing to Bayesian Optimization
Nº de artículo:
185827877
NIO 2900
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What Stands Out
Comprehensive Guide
This book provides a thorough exploration of experimentation techniques, ideal for engineers wishing to master A/B testing, Bayesian optimization, and data-driven decision-making.
Practical Application
Offers real-world examples, ensuring engineers can directly apply concepts and methodologies to enhance project outcomes and improve product performance.
Diverse Techniques
Covers various experimental approaches, equipping readers with the tools to tackle unique challenges within engineering fields and outperform competitors.
Detalles de producto
- 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 | 20 Feb. 2023 |
| Edition | 1st |
| Language | English |
| Print length | 248 pages |
| ISBN-10 | 1617298158 |
| ISBN-13 | 978-1617298158 |
| Item weight | 431 g |
| Dimensions | 18.73 x 0.76 x 23.5 cm |
Who Should Buy?
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Data Scientists
Data scientists looking to enhance their experimentation skills will find practical techniques and methodologies in this resource.
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Product Managers
Product managers interested in optimizing product features through experimentation will benefit from insights on A/B testing and Bayesian methods.
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Engineers Focused on Optimization
Engineers seeking to improve their understanding of statistical methods for optimization will find valuable information and applications here.
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Beginners in Statistics
Individuals with no background in statistics may struggle with the concepts covered in this advanced experimentation guide.
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Software Testing Editorial Review
Experimentation For Engineers: From A/B Testing To Bayesian Optimization is a comprehensive guide published by Manning Publications that delves into advanced experimentation methodologies. Released on February 20, 2023, this 248-page book provides valuable insights into A/B testing and Bayesian optimization, offering engineers the tools they need to make data-driven decisions. Readers have praised its clear writing style and practical examples, which enhance understanding and application of these techniques in real-world scenarios. The book's detailed exploration of experimentation makes it a must-read for engineers looking to refine their skills in this important area.
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ventajas
- Well-structured and informative content
- Practical examples enhance understanding
- Clear writing style appeals to readers
- Covers both A/B testing and Bayesian optimization
- Comprehensive resource for engineers
Contras
- Some readers may find it dense for quick reference
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características y beneficios
- 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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