Название: Building Recommendation Systems in Python and JAX: Hands-On Production Systems at Scale (4th Early Release)Автор: Bryan Bischof, Hector Yee
Издательство: O’Reilly Media, Inc.
Год: 2023-02-08
Язык: английский
Формат: epub (true), pdf
Размер: 10.1 MB
Implementing and designing systems that make suggestions to users are among the most popular and essential machine learning applications available. Whether you want customers to find the most appealing items at your online store, videos to enrich and entertain them, or news they need to know, recommendation systems (RecSys) provide the way.
In this practical book, authors Bryan Bischof and Hector Yee illustrate the core concepts and examples to help you create a RecSys for any industry or scale. You'll learn the math, ideas, and implementation details you need to succeed. This book includes the RecSys platform components, relevant MLOps tools in your stack, plus code examples and helpful suggestions in PySpark, SparkSQL, FastAPI, Weights & Biases, and Kafka.
Implementing and designing systems which provide suggestions to users is among the most popular and most essential applications of Machine Learning to any business. Whether you wish to help your users find the best clothing to match their tastes, the most appealing items to buy from an online store, videos to enrich and entertain them, surface maximally engaging content from their networks, or the news highlights they need to know on that day, recommendation systems provide the way.
Modern recommendation system (often abbreviated RecSys) designs are as diverse as the domains they serve. RecSys consist of the computer software architectures to implement and execute such product goals in addition to the algorithmic components of ranking. Methods for ranking recommendions can come from traditional statistical learning algorithms, linear algebraic inspirations, geometric considerations, and, of course, gradient based methods. Just as the algorithmic methods are diverse, so too are the modeling and evaluation considerations for recommending: personalized ranking, search recommendations, sequence modeling, and the scoring for all of the above are now need-to-know for the working ML Engineer in the space of recommendation systems.
You'll learn:
The data essential for building a RecSys
How to frame your data and business as a RecSys problem
Ways to evaluate models appropriate for your system
Methods to implement, train, test, and deploy the model you choose
Metrics you need to track to ensure your system is working as planned
How to improve your system as you learn more about your users, products, and business case
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