Book Image

Solutions Architect’s Handbook - Second Edition

By : Saurabh Shrivastava, Neelanjali Srivastav
4 (2)
Book Image

Solutions Architect’s Handbook - Second Edition

4 (2)
By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect requires a hands-on approach, and this edition of the Solutions Architect's Handbook brings exactly that. This handbook will teach you how to create robust, scalable, and fault-tolerant solutions and next-generation architecture designs in a cloud environment. It will also help you build effective product strategies for your business and implement them from start to finish. This new edition features additional chapters on disruptive technologies, such as Internet of Things (IoT), quantum computing, data engineering, and machine learning. It also includes updated discussions on cloud-native architecture, blockchain data storage, and mainframe modernization with public cloud. The Solutions Architect's Handbook provides an understanding of solution architecture and how it fits into an agile enterprise environment. It will take you through the journey of solution architecture design by providing detailed knowledge of design pillars, advanced design patterns, anti-patterns, and the cloud-native aspects of modern software design. By the end of this handbook, you'll have learned the techniques needed to create efficient architecture designs that meet your business requirements.
Table of Contents (22 chapters)
20
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21
Index

Summary

In this chapter, you learned about ML architecture and components for a ML workflow. You learned about how data and ML go hand in hand. It is essential to get high-quality data with feature engineering to build the right ML model.

You learned about ML model validation by recognizing model overfit versus underfit situations. You also learned about various supervised and unsupervised ML algorithms. As the cloud is becoming a go-to platform for ML model training and deployment, you learned about ML platforms in popular public cloud providers.

Further, you learned about the ML workflow, including data preprocessing, modeling, evaluation, and prediction. Also, you learned about building ML architecture with a detailed reference architecture built in AWS cloud platforms. MLOps is essential for putting ML models in production. You learned about MLOps principles and best practices. Further, you got an overview of deep learning, which helps solve complex problems by mimicking...