Verificar estado da encomenda

Faça parte de uma comunidade de amantes de livros de todo o mundo e tenha acesso a uma série de benefícios. Crie uma conta gratuitamente

Correio DHL 7.99 € Correio DPD 4.49 € Ponto DPD 3.99 € Correio GLS 5.49 € Correio MRW 5.49 € Ponto GLS 4.49 €

Contacto

Como comprar

Ajuda

A minha conta

▸ Vazio :-(

MLOps Engineering at Scale

Name: MLOps Engineering at Scale
Brand: Manning Publications
SKU: 33298874
Price: 57.95 EUR
Availability: InStock
Author: Carl Osipov
ISBN: 9781617297762

Carl Osipov

Língua

Inglês

Livro Capa mole

Código Libristo: 33298874

Editoras Manning Publications, março 2022

Deploying a machine learning model into a fully realized production system usually requires painst... Descrição completa

Código Libristo: 33298874

140 b

57.95 €

50% de hipóteses Procuremos no mundo Quando vou receber o meu livro?

Até 30 dias para devoluções

Os clientes também compraram

Practical MLOps Noah Gift

Capa mole

67.16 €

Introducing MLOps Clement Stenac

Capa mole

49.56 €

Effective Platform Engineering Sean Alvarez

Capa mole

58.16 €

Designing Machine Learning Systems Chip Huyen

Capa mole

49.56 €

Practices of the Python Pro Dane Hillard

Capa mole

64.43 €

HOW LARGE LANGUAGE MODELS WORK RAFF EDWARD

Capa mole

48.65 €

Machine Learning Engineering in Action Ben Wilson

Capa mole

70.50 €

AI Engineering Chip Huyen

Capa mole

58.16 €

Implementing Mlops in the Enterprise: A Production-First Approach Noah Gift

Capa mole

59.88 €

The Mom Test Rob Fitzpatrick

Capa mole

18.70 €

The Creative Act Rick Rubin

Livro de capa dura

17.39 €

Learning Ray Max Pumperla

Capa mole

49.56 €

Learning Modern Linux Michael Hausenblas

Capa mole

49.56 €

Generative AI Design Patterns Hannes Hapke

Capa mole

59.88 €

Building AI Agents with LLMs, RAG, and Knowledge Graphs Gabriele Iuculano

Capa mole

58.16 €

KNOWLEDGE GRAPHS & LLMS IN ACTION NEGRO ALESSANDRO

Capa mole

58.16 €

Language Lover's Puzzle Book Alex Bellos

Capa mole

11.62 €

Data Pipelines Pocket Reference James Densmore

Capa mole

23.15 €

Data Science at the Command Line Jeroen Janssens

Capa mole

49.56 €

AI AGENTS IN ACTION LANHAM MICHEAL

Capa mole

58.56 €

LLMOps Lucas Meyer

Capa mole

59.88 €

Prompt Engineering for Llms Albert Ziegler

Capa mole

58.16 €

Building a Second Brain: A Proven Method to Organize Your Digital Life and Unlock Your Creative Potential FORTE TIAGO

Livro de capa dura

23.15 €

Demand Forecasting Best Practices Vandeput

Capa mole

70.50 €

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 technology

Your 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 reader

For 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.