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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

MLOps

An ML workflow is a set of operations developed and executed to produce a mathematical model, which eventually is designed to solve a real-world problem. But these models have no value until they are deployed in production other than proofs of concept. ML models almost always require deployment to a production environment to provide business value.

At its core, MLOps fundamentally focuses on transitioning an experimental ML model into a fully operational production system. MLOps is an emerging practice, different from traditional DevOps due to the unique nature of the ML development life cycle and the specific ML artifacts it produces. The ML life cycle revolves around discerning patterns from training data, making the MLOps workflow particularly sensitive to changes in data, as well as variations in data volumes and quality.

A well-developed MLOps practice should support the monitoring of ML life cycle activities as well as the ongoing supervision of models once they...

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