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AWS for Solutions Architects

AWS for Solutions Architects - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed
4.3 (62)
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AWS for Solutions Architects

AWS for Solutions Architects

4.3 (62)
By: Saurabh Shrivastava, Neelanjali Srivastav, Alberto Artasanchez, Imtiaz Sayed

Overview of this book

The second edition of AWS for Solutions Architects provides a practical guide to designing cloud solutions that align with industry best practices. This updated edition covers the AWS Well-Architected Framework, core design principles, and cloud-native patterns to help you build secure, high-performance, and cost-effective architectures. Gain a deep understanding of AWS networking, hybrid cloud connectivity, and edge deployments. Explore big data processing with EMR, Glue, Kinesis, and MSK, enabling you to extract valuable insights from data efficiently. New chapters introduce CloudOps, machine learning, IoT, and blockchain, equipping you with the knowledge to develop modern cloud solutions. Learn how to optimize AWS storage, implement containerization strategies, and design scalable data lakes. Whether working on simple configurations or complex enterprise architectures, this guide provides the expertise needed to solve real-world cloud challenges and build reliable, high-performing AWS solutions.
Table of Contents (19 chapters)
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Other Books You May Enjoy
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Index

What is AI/ML?

ML is a type of computer technology that allows software to improve its performance automatically by learning from data without being explicitly programmed. It is a way of teaching computers to recognize patterns and make predictions based on examples. In simple terms, ML is a way for computers to learn from data and make predictions or decisions. There are several types of ML, each with its unique characteristics and use cases. The main types of ML include:

  • Supervised Learning: Supervised learning is the most widespread form of ML, involving training a model on a labeled dataset to predict the output for new, unseen data. Linear regression, logistic regression, and decision trees are some examples of supervised learning algorithms.
  • Unsupervised Learning: Unsupervised learning, on the other hand, does not use labeled data and instead discovers patterns and structures in the input data. Examples of unsupervised learning algorithms include clustering...
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