Numéro d'article:
216097779
Designing Machine Learning Systems: An Iterative Process for Production-ready Applications
Numéro d'article:
216097779
XAF 46715
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Learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environments and business requirements.
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Ce qui se démarque
Iterative Framework
Offers a systematic approach to developing machine learning systems, ensuring continuous improvement and adaptability to evolving project needs.
Production-Readiness
Focuses on transforming prototypes into reliable applications, emphasizing practicality and scalability for real-world usage, addressing common deployment challenges.
Comprehensive Guidance
Provides valuable insights and practical tips for practitioners, making it accessible for both beginners and experienced developers aiming for high-quality ML solutions.
Détails du produit
- Written by experts from O'Reilly, a leading publisher in technology and business
- Designed for individuals who want to leverage machine learning to solve real-world problems
- Caters to ML engineers, data scientists, data engineers, ML platform engineers, and engineering managers
- Addresses scenarios such as deploying and updating models, automation, bias detection, and ML system responsibility
- Also beneficial for tool developers, individuals seeking ML-related roles, and technical and business leaders
- Assumes basic understanding of various ML models, techniques, metrics, statistical concepts, and common ML tasks
| Publishers | O'Reilly Media |
| Date of publication | May 31, 2022 |
| Prints | 1. |
| Language | English |
| Print Length | 360 pages |
| ISBN-10 | 1098107969 |
| ISBN-13 | 978-1098107963 |
| Dimensions | 17.53 x 1.78 x 23.11 cm |
À qui est-ce destiné ?
-
Data Scientists
Perfect for data scientists looking to implement machine learning systems efficiently and iteratively in production environments.
-
Machine Learning Engineers
Beneficial for engineers who need guidance on building scalable and maintainable machine learning applications.
-
Tech Managers
Ideal for managers overseeing ML projects needing insights on best practices for deployment and project management.
-
Beginners
Not suitable for complete beginners without prior knowledge in machine learning concepts and techniques.
DESCRIPTION DU PRODUIT
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Questions et réponses des clients
-
question:
Who is the author of this book?
répondre: The author of this book is Chip Huyen. -
question:
What are some of the scenarios addressed in this book?
répondre: The book helps in scenarios such as engineering data, automating processes, developing responsible ML systems, etc. -
question:
What is the iterative framework used in this book?
répondre: The iterative framework uses actual case studies backed by ample references.
İngilizce Baskı Chip Huyen Format: Kağıt Kapak Editorial Review
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Avis et évaluations clients
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Avantages
- Clear and practical guidance
- Focus on real-world applications
- Detailed iterative process
- Engaging writing style
- Suitable for various skill levels
Les inconvénients
- Some examples may be too specific.
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Caractéristiques et avantages
- Design ML systems that are reliable and adaptable
- Learn to process and create training data
- Automate the process for continually developing, evaluating, deploying, and updating models
- Develop a monitoring system to detect and address production issues
- Architect an ML platform that serves across use cases
- Develop responsible ML systems
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