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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

Building machine learning architecture

Building a robust and scalable workflow from a loose collection of code is a complex and time-consuming process, and many data scientists need to gain experience building workflows. An ML workflow can be defined as an orchestrated sequence that involves multiple steps. Data scientists and ML developers first need to package numerous code recipes and then specify the order in which they should execute, keeping track of code, data, and model parameter dependencies between each step.

Added complexity in ML workflows warrants monitoring changes in data used for training and predictions because changes in the data could introduce bias, leading to inaccurate predictions. In addition to monitoring the data, data scientists and ML developers also need to monitor model predictions to ensure they are accurate and don’t become skewed toward particular results over time. As a result, it can take several months of custom coding to get the individual...

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