{"product_id":"9781617297762","title":"MLOps Engineering at Scale","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eOsipov, Carl\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e3\/1\/2022\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eDodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eMLOps Engineering at Scale\u003c\/i\u003e you will learn:\u003cbr\u003e\u003cbr\u003eExtracting, transforming, and loading datasets\u003cbr\u003eQuerying datasets with SQL\u003cbr\u003eUnderstanding automatic differentiation in PyTorch\u003cbr\u003eDeploying model training pipelines as a service endpoint\u003cbr\u003eMonitoring and managing your pipeline’s life cycle\u003cbr\u003eMeasuring performance improvements\u003cbr\u003e\u003cbr\u003e\u003ci\u003eMLOps Engineering at Scale\u003c\/i\u003e shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003eA production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003ci\u003eMLOps Engineering at Scale\u003c\/i\u003e teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production.\u003cbr\u003e\u003cbr\u003eWhat's inside\u003cbr\u003e\u003cbr\u003eReduce or eliminate ML infrastructure management\u003cbr\u003eLearn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow\u003cbr\u003eDeploy training pipelines as a service endpoint\u003cbr\u003eMonitor and manage your pipeline’s life cycle\u003cbr\u003eMeasure performance improvements\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eReaders need to know Python, SQL, and the basics of machine learning. No cloud experience required.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eCarl Osipov\u003c\/b\u003e implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePART 1 - MASTERING THE DATA SET\u003cbr\u003e1 Introduction to serverless machine learning\u003cbr\u003e2 Getting started with the data set\u003cbr\u003e3 Exploring and preparing the data set\u003cbr\u003e4 More exploratory data analysis and data preparation\u003cbr\u003ePART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING\u003cbr\u003e5 Introducing PyTorch: Tensor basics\u003cbr\u003e6 Core PyTorch: Autograd, optimizers, and utilities\u003cbr\u003e7 Serverless machine learning at scale\u003cbr\u003e8 Scaling out with distributed training\u003cbr\u003ePART 3 - SERVERLESS MACHINE LEARNING PIPELINE\u003cbr\u003e9 Feature selection\u003cbr\u003e10 Adopting PyTorch Lightning\u003cbr\u003e11 Hyperparameter optimization\u003cbr\u003e12 Machine learning pipeline","brand":"Manning","offers":[{"title":"Default Title","offer_id":48176692396287,"sku":"9781617297762","price":49.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_9752383d-1dae-4b76-a8f6-56189b409315.jpg?v=1771351510","url":"https:\/\/massivebookshop.com\/products\/9781617297762","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}