Envío gratis a partir de 69,99 euros.

«Comprobar estado del pedido»

Entre a formar parte de una comunidad de amantes de los libros del mundo entero y acceda a un sinfín de ventajas. Crear una cuenta gratis

Envío gratuito con Zásilkovna para compras superiores a 59.99 €

Mensajería SEUR 4.99 € Mensajería GLS 7.99 € Mensajería Correos 5.49 € Mensajería DHL 5.49 € Punto SEUR 3.99 €

Contacto

Cómo comprar

Ayuda

Mi cuenta

▸ Vacío :-(

Envío gratis a partir de 69,99 euros.

MLOps Engineering at Scale

Name: MLOps Engineering at Scale
Brand: Manning Publications
SKU: 33298874
Price: 58.49 EUR
Availability: InStock
Author: Carl Osipov
ISBN: 9781617297762

Carl Osipov

Idioma

Inglés

Libro Tapa blanda

Código Libristo: 33298874

Editores Manning Publications, marzo 2022

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

Código Libristo: 33298874

143 b

58.49 €

50% de probabilidad Buscaremos por todo el mundo ¿Cuándo recibiré mi libro?

Política de devolución de 30 días

Clientes que también han comprado

Practical MLOps Noah Gift

Tapa blanda

70.59 €

Introducing MLOps Clement Stenac

Tapa blanda

52.09 €

Effective Platform Engineering Sean Alvarez

Tapa blanda

60.29 €

Designing Machine Learning Systems Chip Huyen

Tapa blanda

52.09 €

Practices of the Python Pro Dane Hillard

Tapa blanda

64.99 €

HOW LARGE LANGUAGE MODELS WORK RAFF EDWARD

Tapa blanda

50.49 €

Machine Learning Engineering in Action Ben Wilson

Tapa blanda

71.19 €

AI Engineering Chip Huyen

Tapa blanda

62.29 €

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

Tapa blanda

62.89 €

The Mom Test Rob Fitzpatrick

Tapa blanda

20.09 €

The Creative Act Rick Rubin

Tapa dura

19.69 €

Learning Ray Max Pumperla

Tapa blanda

52.09 €

Learning Modern Linux Michael Hausenblas

Tapa blanda

52.09 €

Generative AI Design Patterns Hannes Hapke

Tapa blanda

62.89 €

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

Tapa blanda

60.29 €

KNOWLEDGE GRAPHS & LLMS IN ACTION NEGRO ALESSANDRO

Tapa blanda

60.29 €

Language Lover's Puzzle Book Alex Bellos

Tapa blanda

12.29 €

Data Pipelines Pocket Reference James Densmore

Tapa blanda

24.29 €

Data Science at the Command Line Jeroen Janssens

Tapa blanda

52.09 €

AI AGENTS IN ACTION LANHAM MICHEAL

Tapa blanda

46.79 €

LLMOps Lucas Meyer

Tapa blanda

62.89 €

Prompt Engineering for Llms Albert Ziegler

Tapa blanda

61.29 €

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

Tapa dura

24.29 €

Demand Forecasting Best Practices Vandeput

Tapa blanda

71.19 €

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.