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

Building machine learning architecture

Creating an ML pipeline consists of multiple phases and requires iterative improvement. Building a robust and scalable workflow from a loose collection of code is a complex and time-consuming process, and many data scientists don't have 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 they should execute, keeping track of code, data, and model parameter dependencies between each step.

Added complexity to 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...