MLOps Engineering at Scale (Paperback)
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MLOps Engineering at Scale (Paperback)
MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors.
MLOps Engineering at Scale (Paperback)
Artigo n.º: 96859891

MLOps Engineering at Scale (Paperback)

Artigo n.º: 96859891

STD 1674800

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MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors.
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O que se Destaca

Comprehensive Guide
Offers extensive coverage of MLOps principles, frameworks, and tools, providing readers with in-depth knowledge crucial for implementing machine learning operations effectively at scale.
Practical Techniques
Equips practitioners with actionable strategies and real-world case studies, ensuring readers can apply concepts effectively and overcome common challenges in the deployment of machine learning models.
Expert Insights
Authored by industry experts, it delivers authoritative perspectives and best practices, making it an essential reference for both beginners and experienced professionals in MLOps.

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  • Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system's infrastructure. Following a real-world use case for calculating taxi fares, you'll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware. about the technologyYour new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure. Serverless machine learning offers a productivity-boosting alternative. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, you're free to focus on tuning and improving your models. about the book Cloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You'll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you'll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you'll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you're done, you'll have the tools to easily bridge the gap between ML models and a fully functioning production system. what's inside Extracting, transforming, and loading datasets Querying datasets with SQL Understanding automatic differentiation in PyTorch Deploying trained models and pipelines as a service endpoint Monitoring and managing your pipeline's life cycle Measuring performance improvements about the readerFor data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required. about the author Carl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world's foremost experts in machine learning and also helped manage the company's efforts to democratize artificial intelligence. You can learn more about Carl from his blog Clouds With Carl.
Book formatPaperback
Fiction/nonfictionNon-Fiction
GenreNonfiction
Publication dateMarch, 2022
Pages250
Reading levelGeneral
SubgenreComputers/Data Science - Machine Learning
EditionPaperback
PublisherPearson Education
Original languagesEnglish
LanguageEnglish
Edu focusEngineering
Awards wonthree corporate technology awards from IBM
Digital file formatPDF, Kindle, ePub
Digital reader formatPDF, Kindle, and ePub
Digital audio file formatPDF, Kindle, and ePub
Retail packagingSingle Piece
Assembled product height9.21 in
Assembled product weight1.25 lb (570 grams)
Bisac subject headingComputers

Quem Deverá Comprar?

Suitable For
  • Data Scientists

    Provides essential MLOps practices enabling data scientists to collaborate effectively in deploying machine learning models.

  • Machine Learning Engineers

    Offers in-depth methodologies for engineers to streamline workflows, enhance productivity, and manage complex models efficiently.

  • Team Leads

    Equips team leads with the knowledge to oversee teams implementing scalable MLOps solutions within organizations.

Not Suitable For
  • Beginners

    May be too advanced for individuals new to machine learning or MLOps concepts without prior experience.

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  • Pergunta: Como comprar MLOps Engineering at Scale (Paperback) online na Ubuy?

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  • Pergunta: MLOps Engineering at Scale (Paperback) está disponível para compras online em São Tomé and Príncipe?

    Resposta: Sim, na Ubuy São Tomé and Príncipe este produto está disponível para você comprar a um preço razoável.. O MLOps Engineering at Scale (Paperback) não está disponível localmente, mas você pode confiar em nós com nossos serviços de remessa expressa.
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Carl Osipov All Books Editorial Review

MLOps Engineering at Scale (Paperback) offers a comprehensive exploration of MLOps principles, ideal for those looking to deepen their understanding of machine learning operations. This non-fiction work, published by Pearson Education in March 2022, spans 250 pages and is written in English. The book is designed for general readers interested in the engineering aspect of computing, providing a valuable resource in modern data practices. Its digital formats, including PDF, Kindle, and ePub, ensure accessibility for various reading preferences, making it a versatile addition to any tech enthusiast's library.

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Prós

  • Accessible in multiple digital formats
  • In-depth exploration of MLOps principles
  • Suitable for general readers
  • Well-structured for easy comprehension
  • Published by a reputable provider

Contras

  • May be too technical for beginners

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STD 1674800

Encomende já e receba por volta de Quinta-feira, Outubro 22

Qtd.:

Restam apenas 10 artigos em stock.
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Os nossos principais Parceiros Logísticos
  • fedex
  • dhl
Entrega internacional mais rápida

Recursos e benefícios

  • Dodge costly infrastructure tasks with MLOps.
  • Rapidly bring machine learning models to production.
  • Learn to deploy model training pipelines as service endpoints.
  • Use AWS and cloud services to reduce development time.
  • Includes a free eBook with your purchase.
  • Ideal for Python and SQL users, no cloud experience needed.

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