{"product_id":"mlops-engineering-at-scale","title":"MLOps Engineering at Scale","description":"\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003eIn \u003c\/span\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eMLOps Engineering at Scale\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e you will learn:\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e    Extracting, transforming, and loading datasets\u003cbr style=\"box-sizing: border-box;\"\u003e    Querying datasets with SQL\u003cbr style=\"box-sizing: border-box;\"\u003e    Understanding automatic differentiation in PyTorch\u003cbr style=\"box-sizing: border-box;\"\u003e    Deploying model training pipelines as a service endpoint\u003cbr style=\"box-sizing: border-box;\"\u003e    Monitoring and managing your pipeline’s life cycle\u003cbr style=\"box-sizing: border-box;\"\u003e    Measuring performance improvements\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eMLOps Engineering at Scale\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\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 style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003ePurchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the technology\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\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 style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the book\u003c\/strong\u003e\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-italic\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-style: italic\u003eMLOps Engineering at Scale\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\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 style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eWhat's inside\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e    Reduce or eliminate ML infrastructure management\u003cbr style=\"box-sizing: border-box;\"\u003e    Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow\u003cbr style=\"box-sizing: border-box;\"\u003e    Deploy training pipelines as a service endpoint\u003cbr style=\"box-sizing: border-box;\"\u003e    Monitor and manage your pipeline’s life cycle\u003cbr style=\"box-sizing: border-box;\"\u003e    Measure performance improvements\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the reader\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003eReaders need to know Python, SQL, and the basics of machine learning. No cloud experience required.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eAbout the author\u003c\/strong\u003e\u003c\/span\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cspan class=\"a-text-bold\" style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size: font-weight:\u003eCarl Osipov\u003c\/span\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM.\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003ePART 1 - MASTERING THE DATA SET\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e1 Introduction to serverless machine learning\u003cbr style=\"box-sizing: border-box;\"\u003e2 Getting started with the data set\u003cbr style=\"box-sizing: border-box;\"\u003e3 Exploring and preparing the data set\u003cbr style=\"box-sizing: border-box;\"\u003e4 More exploratory data analysis and data preparation\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003ePART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e5 Introducing PyTorch: Tensor basics\u003cbr style=\"box-sizing: border-box;\"\u003e6 Core PyTorch: Autograd, optimizers, and utilities\u003cbr style=\"box-sizing: border-box;\"\u003e7 Serverless machine learning at scale\u003cbr style=\"box-sizing: border-box;\"\u003e8 Scaling out with distributed training\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e\u003cspan style=\"box-sizing: border-box; color: rgb(15, 17, 17); font-family: \" amazon ember arial sans-serif font-size:\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e\u003cstrong\u003ePART 3 - SERVERLESS MACHINE LEARNING PIPELINE\u003c\/strong\u003e\u003cbr style=\"box-sizing: border-box;\"\u003e9 Feature selection\u003cbr style=\"box-sizing: border-box;\"\u003e10 Adopting PyTorch Lightning\u003cbr style=\"box-sizing: border-box;\"\u003e11 Hyperparameter optimization\u003cbr style=\"box-sizing: border-box;\"\u003e12 Machine learning pipeline\u003c\/span\u003e\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":50506261758022,"sku":"9781617297762","price":3939.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50506261790790,"sku":"9781617297762-2","price":4333.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/41b1vuC1KtL._SX397_BO1_204_203_200.jpg?v=1790324517","url":"https:\/\/cb-india.com\/products\/mlops-engineering-at-scale","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}