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Item #:
34602106
Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
87% of respondents would recommend this to a friend
Item #:
34602106
KRW 21525
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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.
Product Details
| 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?
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Data Scientists
Ideal for data scientists looking to implement AI and machine learning pipelines on AWS effectively and efficiently.
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Cloud Engineers
Beneficial for cloud engineers who want to understand integrating AWS services into data science projects and workflows.
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Tech Instructors
Useful for educators teaching data science or machine learning, providing practical examples and hands-on AWS integration.
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Beginners
Not suitable for absolute beginners; foundational knowledge in data science and AWS is prerequisite for comprehension.
Product Description
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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Pros
- 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
Cons
- Expensive
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