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Solutions Architect's Handbook

Solutions Architect's Handbook - Third Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4.7 (56)
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Solutions Architect's Handbook

Solutions Architect's Handbook

4.7 (56)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Build a strong foundation in solution architecture and excel in your career 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. From large language models and generative AI to deep learning innovations, these cutting-edge advancements are shaping the future of technology. Key topics such as cloud-native architecture, data engineering architecture, cloud optimization, mainframe modernization, and building cost-efficient, secure architectures remain essential today. This book covers both emerging and foundational technologies, guiding you through solution architecture design with key principles and providing the knowledge you need to succeed as a Solutions Architect. It also sharpens your soft skills, providing career-accelerating techniques to stay ahead. By the end of this book, you will be able to harness cutting-edge technologies, apply 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
Other Books You May Enjoy
19
Index

Machine learning in the cloud

ML development is a complex and costly process. There are barriers to adoption at each step of the ML workflow, from collecting and preparing data, which is time consuming and undifferentiated, to choosing the right ML algorithm, which is often done by trial and error, and lengthy training times, which leads to higher costs. Then there is model tuning, which can be a very long cycle and requires adjusting thousands of different combinations. Once you’ve deployed a model, you must monitor it and then scale and manage its production.

To solve these challenges, all major public cloud vendors provide an ML platform that facilitates ease of training, tuning, and deploying ML models anywhere at a low cost. For example, Amazon SageMaker is one of the most popular platforms that provides end-to-end ML services. SageMaker provides users with an integrated workbench of tools brought together in one place through SageMaker Studio. Users can launch Jupyter...

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Solutions Architect's Handbook
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