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제품번호 #:
38043469
MLOps Engineering at Scale
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제품번호 #:
38043469
KRW 103601
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제품 세부 정보
- Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools!In MLOps Engineering at Scale you will learn:Extracting, transforming, and loading datasetsQuerying datasets with SQLUnderstanding automatic differentiation in PyTorchDeploying model training pipelines as a service endpointMonitoring and managing your pipeline’s life cycleMeasuring performance improvementsMLOps Engineering at Scale 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.About the technologyA 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.About the bookMLOps Engineering at Scale 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.What's insideReduce or eliminate ML infrastructure managementLearn state-of-the-art MLOps tools like PyTorch Lightning and MLFlowDeploy training pipelines as a service endpointMonitor and manage your pipeline’s life cycleMeasure performance improvementsAbout the readerReaders need to know Python, SQL, and the basics of machine learning. No cloud experience required.About the authorCarl Osipov implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM.Table of ContentsPART 1 - MASTERING THE DATA SET1 Introduction to serverless machine learning2 Getting started with the data set3 Exploring and preparing the data set4 More exploratory data analysis and data preparationPART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING5 Introducing PyTorch: Tensor basics6 Core PyTorch: Autograd, optimizers, and utilities7 Serverless machine learning at scale8 Scaling out with distributed trainingPART 3 - SERVERLESS MACHINE LEARNING PIPELINE9 Feature selection10 Adopting PyTorch Lightning11 Hyperparameter optimization12 Machine learning pipeline
| Publisher | Manning |
| Publication date | March 1, 2022 |
| Language | English |
| Print length | 344 pages |
| ISBN-10 | 1617297763 |
| ISBN-13 | 978-1617297762 |
| Item Weight | 1.25 pounds (570 grams) |
| Dimensions | 7.38 x 0.7 x 9.25 inches (18.7 x 1.8 x 23.5 cm) |
제품 설명
MLOps Engineering at Scale
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