{"product_id":"scalable-machine-learning-architectures-best-practices-for-handling-big-data-and-distributed-systems-9798307714768","title":"Scalable Machine Learning Architectures: Best Practices for Handling Big Data and Distributed Systems","description":"\u003cp\u003e • Author(s): Greyson Chesterfield\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Artificial Intelligence - Expert Systems\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e\"Scalable Machine Learning Architectures: Best Practices for Handling Big Data and Distributed Systems\"\u003c\/b\u003e is the definitive guide for data scientists, machine learning engineers, and architects aiming to build and deploy machine learning systems that can scale seamlessly with the demands of big data and modern distributed systems. In today's world of rapidly growing data volumes, creating scalable and efficient machine learning pipelines is critical to success.\u003c\/p\u003e\u003cp\u003eThis book provides a hands-on approach to designing machine learning architectures that are robust, efficient, and ready to handle real-world challenges. From implementing distributed training techniques to optimizing data pipelines, you'll learn how to leverage state-of-the-art tools and platforms such as TensorFlow, PyTorch, Apache Spark, Kubernetes, and more.\u003c\/p\u003e\u003cp\u003eThrough real-world examples and actionable strategies, \u003cb\u003e\"Scalable Machine Learning Architectures\"\u003c\/b\u003e equips you to address scalability issues, improve model performance, and ensure efficient resource utilization.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside this book, you'll learn how to: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eDesign end-to-end machine learning workflows that scale effortlessly.\u003c\/li\u003e\n\u003cli\u003eImplement distributed training across GPUs and TPUs for large datasets.\u003c\/li\u003e\n\u003cli\u003eOptimize data preprocessing with tools like Apache Spark and Hadoop.\u003c\/li\u003e\n\u003cli\u003eDeploy machine learning models on Kubernetes, Docker, and cloud platforms.\u003c\/li\u003e\n\u003cli\u003eUse feature stores and model registries to manage scalable pipelines.\u003c\/li\u003e\n\u003cli\u003eMonitor and maintain production-grade systems with ML observability tools.\u003c\/li\u003e\n\u003cli\u003eHandle challenges in big data environments, such as latency, fault tolerance, and data sharding.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhether you're building recommendation systems, real-time prediction engines, or large-scale natural language processing applications, this book provides the roadmap to tackle the challenges of scaling machine learning in a data-intensive world.\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":45556218626199,"sku":"9798307714768","price":1786.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798307714768.webp?v=1768591056","url":"https:\/\/atlanticbooks.com\/products\/scalable-machine-learning-architectures-best-practices-for-handling-big-data-and-distributed-systems-9798307714768","provider":"Atlantic Books","version":"1.0","type":"link"}