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Solutions Architect's Handbook

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
4.7 (59)
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Solutions Architect's Handbook

Solutions Architect's Handbook

4.7 (59)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Master the art of solution architecture and excel as a Solutions Architect with the Solutions Architect's Handbook. Authored by seasoned AWS technology leaders Saurabh Shrivastav and Neelanjali Srivastav, this book goes beyond traditional certification guides, offering in-depth insights and advanced techniques to meet the specific needs and challenges of solutions architects today. This edition introduces exciting new features that keep you at the forefront of this evolving field. Large language models, generative AI, and innovations in deep learning are cutting-edge advancements shaping the future of technology. Topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient and secure architectures remain important in today's landscape. This book provides coverage of these emerging and key technologies and walks you through solution architecture design from key principles, providing you with the knowledge you need to succeed as a Solutions Architect. It will also level up your soft skills, providing career-accelerating techniques to help you get ahead. Unlock the potential of cutting-edge technologies, gain practical insights from real-world scenarios, and enhance your solution architecture skills with the Solutions Architect's Handbook.
Table of Contents (20 chapters)
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18
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19
Index

Summary

In this comprehensive chapter, you journeyed through the fundamental concepts and practical applications of ML. You began by understanding the core principles of ML and its close relationship with data science, emphasizing the pivotal role of data in training and evaluating ML models. You explored different types of ML, ranging from supervised and unsupervised learning to reinforcement learning and deep learning. Each type was elucidated with real-world examples and common algorithms, providing you with an understanding of when and how to apply them.

Next, you delved into the critical concepts of model overfitting and underfitting, exploring the delicate balance required to achieve model generalization. You examined various strategies and techniques to address these challenges effectively.

Popular AI tools and frameworks were covered and the chapter also ventured into cloud-based ML, demonstrating the advantages and capabilities of harnessing cloud platforms for ML...

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