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Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
87% of respondents would recommend this to a friend
IQD 18371
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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
Product Details
- 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?
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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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IQD 18371
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Features & Benefits
- Demonstrate how to reduce cost and improve performance.
- Apply the AI and ML stack to real-world use cases.
- Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot.
- Explore real-time ML, anomaly detection, and streaming analytics on data streams with Kinesis and Managed Streaming for Apache Kafka.
- Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more.
- Learn about the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment.
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