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Data Science On Aws: Implementing Endtoend, Continuous Ai And Machine Learning Pipelines
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With This Practical Book, Ai And Machine Learning Practitioners Will Learn How To Successfully Build And Deploy Data Science Projects On Amazon Web Services. The Amazon Ai And Machine Learning Stack Unifies Data Science, Data Engineering, And Application Development To Help Level Up Your Skills. This Guide Shows You How To Build And Run Pipelines In The Cloud, Then Integrate The Results Into Applications In Minutes Instead Of Days. Throughout The Book, Authors Chris Fregly And Antje Barth Demonstrate How To Reduce Cost And Improve Performance. Apply The Amazon Ai And Ml Stack To Realworld Use Cases For Natural Language Processing, Computer Vision, Fraud Detection, Conversational Devices, And More Use Automated Machine Learning To Implement A Specific Subset Of Use Cases With Sagemaker Autopilot Dive Deep Into The Complete Model Development Lifecycle For A Bertbased Nlp Use Case Including Data Ingestion, Analysis, Model Training, And Deployment Tie Everything Together Into A Repeatable Machine Learning Operations Pipeline Explore Realtime Ml, Anomaly Detection, And Streaming Analytics On Data Streams With Amazon 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
⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
For more information, please visit www.P65Warnings.ca.gov.
- Q: How many pages does this book have? A: This book has five hundred twenty-one pages. It provides a comprehensive guide on data science projects using Amazon Web Services.
- Q: What are the dimensions of the book? A: The book measures seven point zero one inches in length, one point two six inches in width, and nine point two five inches in height. These dimensions make it portable for easy reading.
- Q: What type of binding does the book have? A: The book is available in paperback binding. This makes it flexible and easier to handle while reading.
- Q: Who is the author of this book? A: The authors of this book are Chris Fregly and Antje Barth. They are experienced practitioners in AI and machine learning.
- Q: What category does this book fall under? A: This book is categorized under statistics. It focuses on implementing AI and machine learning pipelines.
- Q: What is the primary focus of the book? A: The primary focus is on building and deploying data science projects on Amazon Web Services. It includes practical guidance for practitioners.
- Q: How can I apply the concepts from this book? A: You can apply the concepts by following the step-by-step guides provided for building and running ML pipelines. The book offers real-world use cases for practical application.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It provides foundational knowledge and practical examples to help newcomers understand data science on AWS.
- Q: Can I use this book for real-world projects? A: Yes, the book is designed for real-world applications. It includes use cases for natural language processing, computer vision, and more.
- Q: Are there any specific tools mentioned in the book? A: Yes, the book mentions several tools such as Amazon SageMaker for automated machine learning and Amazon Kinesis for real-time analytics. These tools are essential for implementing the concepts.
- Q: What best practices does the book cover? A: The book covers security best practices for data science projects, including identity and access management. It emphasizes the importance of secure workflows.
- Q: How does this book help with machine learning operations? A: The book provides guidance on creating repeatable machine learning operations pipelines. It details the complete model development lifecycle.
- Q: What can I expect to learn about data ingestion? A: You will learn about data ingestion techniques as part of the model development lifecycle. The book covers analysis, training, and deployment processes.
- Q: Does the book address cost reduction strategies? A: Yes, the book discusses strategies for reducing costs while improving performance in data science projects. It offers practical tips throughout.
- Q: Can I find examples of anomaly detection in the book? A: Yes, the book includes examples of anomaly detection and streaming analytics. It explains how to work with data streams effectively.