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96859891
MLOps Engineering at Scale (Paperback)
товар №:
96859891
UZS 870896
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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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What Stands Out
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.
Информация о продукте
- 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 format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Nonfiction |
| Publication date | March, 2022 |
| Pages | 250 |
| Reading level | General |
| Subgenre | Computers/Data Science - Machine Learning |
| Edition | Paperback |
| Publisher | Pearson Education |
| Original languages | English |
| Language | English |
| Edu focus | Engineering |
| Awards won | three corporate technology awards from IBM |
| Digital file format | PDF, Kindle, ePub |
| Digital reader format | PDF, Kindle, and ePub |
| Digital audio file format | PDF, Kindle, and ePub |
| Retail packaging | Single Piece |
| Assembled product height | 9.21 in |
| Assembled product weight | 1.25 lb (570 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
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.
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Team Leads
Equips team leads with the knowledge to oversee teams implementing scalable MLOps solutions within organizations.
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Beginners
May be too advanced for individuals new to machine learning or MLOps concepts without prior experience.
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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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Плюсы
- 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
Минусы
- May be too technical for beginners
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UZS 870896
Закажите сейчас и получите товар приблизительно Пятница, Октябрь 23
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Особенности и преимущества
- 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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