Book Image

Software Architecture with C# 10 and .NET 6 - Third Edition

By : Gabriel Baptista, Francesco Abbruzzese
4 (1)
Book Image

Software Architecture with C# 10 and .NET 6 - Third Edition

4 (1)
By: Gabriel Baptista, Francesco Abbruzzese

Overview of this book

Software architecture is the practice of implementing structures and systems that streamline the software development process and improve the quality of an app. This fully revised and expanded third edition, featuring the latest features of .NET 6 and C# 10, enables you to acquire the key skills, knowledge, and best practices required to become an effective software architect. Software Architecture with C# 10 and .NET 6, Third Edition features new chapters that describe the importance of the software architect, microservices with ASP.NET Core, and analyzing the architectural aspects of the front-end in the applications, including the new approach of .NET MAUI. It also includes a new chapter focused on providing a short introduction to artificial intelligence and machine learning using ML.NET, and updated chapters on Azure Kubernetes Service, EF Core, and Blazor. You will begin by understanding how to transform user requirements into architectural needs and exploring the differences between functional and non-functional requirements. Next, you will explore how to choose a cloud solution for your infrastructure, taking into account the factors that will help you manage a cloud-based app successfully. Finally, you will analyze and implement software design patterns that will allow you to solve common development problems. By the end of this book, you will be able to build and deliver highly scalable enterprise-ready apps that meet your business requirements.
Table of Contents (27 chapters)
24
Answers
25
Other Books You May Enjoy
26
Index

Questions

  1. What search techniques are fundamental in artificial intelligence?
  2. Since induction learning requires fewer examples, why don’t we use it in all problems?
  3. What is the main advantage of neural net expert systems and Bayesian networks over classical expert systems?
  4. Is it true that layered neural nets can approximate any real-valued continuous function?
  5. Is it true that layered neural nets’ learning always converges toward the best solution?
  6. What are the 4 steps for turning an ML project from theory into practice?
  7. How can ML.NET help developers to deliver machine learning solutions?