دانلود کتاب الگوهای یادگیری ماشین توزیع شده

Machine learning systems are becoming more and more important nowadays: recommendation systems learn to generate recommendations of potential interest with the right context according to user feedback and interactions; anomalous event detection systems help monitor assets to avoid downtime due to extreme conditions; fraud detection systems protect financial institutions from security attacks and malicious fraud behaviors. There are increasing demands on building large scale distributed machine learning systems. If a data analyst, data scientist, or software engineer has basic knowledge and hands-on experience in building machine learning models in Python and wants to take a step further to learn how to build something more robust, scalable, and reliable, then this is the right book to read. While experience in production environments or distributed systems is not a requirement, we expect readers in this position to have at least some exposure to machine learning applications running in production and should have written Python and Bash scripts for at least one year. Being able to handle large scale problems and take what’s developed on your personal laptop to large distributed clusters is exciting. This book introduces best practices in various patterns that speed up the development and deployment of machine learning models, leverage automations from different tools, and benefit from hardware accelerations. After reading this book, the reader will be able to: choose and apply the correct patterns for building and deploying distributed machine learning systems; use common tooling, such as TensorFlow, Kubernetes, Kubeflow, and Argo Workflows appropriately within a ML workflow; and gain practical experience for managing and automating machine learning tasks in Kubernetes. A comprehensive, handson project in the last chapter provides an opportunity to build a real-life distributed machine learning system that leverages many of the patterns we learn in the second part of the book. In addition, there are supplemental exercises at the end of some sections in each following chapter to recap what we’ve learned.