Book Image

Solutions Architect's Handbook

By : Saurabh Shrivastava, Neelanjali Srivastav
Book Image

Solutions Architect's Handbook

By: Saurabh Shrivastava, Neelanjali Srivastav

Overview of this book

Becoming a solutions architect gives you the flexibility to work with cutting-edge technologies and define product strategies. This handbook takes you through the essential concepts, design principles and patterns, architectural considerations, and all the latest technology that you need to know to become a successful solutions architect. This book starts with a quick introduction to the fundamentals of solution architecture design principles and attributes that will assist you in understanding how solution architecture benefits software projects across enterprises. You'll learn what a cloud migration and application modernization framework looks like, and will use microservices, event-driven, cache-based, and serverless patterns to design robust architectures. You'll then explore the main pillars of architecture design, including performance, scalability, cost optimization, security, operational excellence, and DevOps. Additionally, you'll also learn advanced concepts relating to big data, machine learning, and the Internet of Things (IoT). Finally, you'll get to grips with the documentation of architecture design and the soft skills that are necessary to become a better solutions architect. By the end of this book, you'll have learned techniques to create an efficient architecture design that meets your business requirements.
Table of Contents (18 chapters)

Understanding supervised and unsupervised ML

In supervised learning, the algorithm is given a set of training examples where the data and target are known. It can then predict the target value for new datasets, containing the same attributes. For supervised algorithms, human intervention and validation are required, for example, in photo classification and tagging.

In unsupervised learning, the algorithm is provided with massive amounts of data, and it must find patterns and relationships between the data. It can then draw inferences from datasets.

In unsupervised learning, human intervention is not required, for example, auto-classification of documents based on context. It addresses the problem, where correct output is not available for training examples, and the algorithm must find patterns in data using clustering.

Reinforcement learning is another category where you don't tell the algorithm what action is correct, but give it a reward or penalty after each action in a sequence...