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

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, to 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...