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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
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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