Learn practical and modern experimental methods used by engineers in technology and trading.
In Experimentation for Engineers: From A/B testing to Bayesian optimization you will learn how to:
Design, run, and analyze an A/B test
Break the "feedback loops" cause by periodic retraining of ML models
Increase experimentation rate with multi-armed bandits
Tune multiple parameters experimentally with Bayesian optimization
Clearly define business metrics used for decision making
Identify and avoid the common pitfalls of experimentation
Experimentation for Engineers: From A/B testing to Bayesian optimization is a toolbox for optimizing machine learning systems, quantitative trading strategies, and more. You’ll start with a deep dive into tests like A/B testing, and then graduate to advanced techniques used to measure performance in highly competitive industries like finance and social media. The tests in this unique, practical guide will quickly reveal which approaches and features deliver real results for your business.
about the technology
Tuning your systems is best done by following established methods employed by high-performing teams like the ones led by author David Sweet. This book reveals experiments, tests, metrics, and industry-tested tools that will ensure your projects are constantly improving, delivering revenue, and ensuring user satisfaction.
about the book
Experimentation for Engineers: From A/B testing to Bayesian optimization teaches you reliable techniques for evaluating new features and fine-tuning parameters. You’ll learn to optimize production systems with methods that have been proven in highly competitive environments. Each method is fully explained using basic math and Python code, and illustrated with real-world use cases in quantitative trading, recommender systems, and ad serving.
You’ll learn how to evaluate the changes you make to your system and ensure that your testing doesn’t undermine revenue or other business metrics. By the time you’re done, you’ll be able to seamlessly deploy experiments in production while avoiding common pitfalls.
about the reader
For ML engineers, quantitative traders, and software engineers looking to extract the most value from their systems. Examples in Python and NumPy.
about the author
David Sweet has worked as a quantitative trader at GETCO and a machine learning engineer at Instagram, where he used experimental methods to tune trading systems and recommender systems. This book is an extension of his lectures at NYU Stern and is the basis for his course, Experimental Optimization, at Yeshiva University.
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