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Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
Build and deploy data science projects on Web Services to level up your skills and reduce cost, while improving performance
Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
商品#: 34602106

Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

商品#: 34602106

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Build and deploy data science projects on Web Services to level up your skills and reduce cost, while improving performance
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What Stands Out

End-to-End Solutions
This book provides comprehensive guidance on implementing complete AI and ML pipelines on AWS, enabling readers to manage everything from data ingestion to model deployment efficiently.
Continuous Learning
It emphasizes the importance of continuous learning and adaptation in AI, showcasing methodologies to keep models updated and relevant in a rapidly evolving landscape.
AWS Best Practices
The book outlines best practices for leveraging AWS services effectively, ensuring readers can maximize performance, scalability, and cost-effectiveness in their data science projects.

產品詳情

Discover the power of Data Science on AWS with our comprehensive guide. Implement continuous AI & machine learning pipelines to drive innovation. Shop at Ubuy Taiwan
  • Practical guide for AI and machine learning practitioners to deploy projects on Amazon Web Services
  • Utilizes Amazon AI and machine learning stack to unify data science, data engineering, and application development
  • Demonstrates cost reduction and performance improvement in data science projects
  • Covers real-world use cases for natural language processing, computer vision, fraud detection, and more
  • Explores automated machine learning with SageMaker Autopilot and machine learning operations pipeline
  • Provides security best practices for data science projects and workflows on AWS
Publisher O'Reilly Media
Publication date May 11, 2021
Edition 1st
Language English
Print length 521 pages
ISBN-10 1492079391
ISBN-13 978-1492079392
Item Weight 2.31 pounds (1.05 kg)
Dimensions 7 x 1.25 x 9.25 inches (17.8 x 3.2 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Scientists

    Ideal for data scientists looking to implement AI and machine learning pipelines on AWS effectively and efficiently.

  • Cloud Engineers

    Beneficial for cloud engineers who want to understand integrating AWS services into data science projects and workflows.

  • Tech Instructors

    Useful for educators teaching data science or machine learning, providing practical examples and hands-on AWS integration.

Not Suitable For
  • Beginners

    Not suitable for absolute beginners; foundational knowledge in data science and AWS is prerequisite for comprehension.

產品描述

Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

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Computer Vision & Pattern Recognition Editorial Review

The book "Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines" contains a lot of practical knowledge on how to use the ML services of AWS and explains what and why. The book is closely tied to the "Practical Data Science on the AWS Cloud" course on Coursera, with the authors being part of the instructor team for this course. However, some customers complain about the book being expensive and printed in black and white on low-quality paper. Despite this, customers found the book well-written and very concise, with a good balance between sample code and explanation. Many related insights are also included. The code repository is being actively maintained. Some readers found the book interesting and would recommend it to anyone who has a basic familiarity with ML concepts and needs to learn how to implement a MLOps pipeline in an AWS cloud environment.

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優點

  • Contains practical knowledge on using ML services of AWS
  • Balanced between sample code and explanation
  • Many related insights included
  • Code repository is actively maintained
  • Provides a good overview of data science jargon, processes, and tools

缺點

  • Expensive

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