Technical Debt Quadrant Martin Fowler Book
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Technical Debt in Practice
The practical implications of technical debt for the entire software lifecycle; with examples and case studies. Technical debt in software is incurred when developers take shortcuts and make ill-advised technical decisions in the initial phases of a project, only to be confronted with the need for costly and labor-intensive workarounds later. This book offers advice on how to avoid technical debt, how to locate its sources, and how to remove it. It focuses on the practical implications of technical debt for the entire software life cycle, with examples and case studies from companies that range from Boeing to Twitter. Technical debt is normal; it is part of most iterative development processes. But if debt is ignored, over time it may become unmanageably complex, requiring developers to spend all of their effort fixing bugs, with no time to add new features--and after all, new features are what customers really value. The authors explain how to monitor technical debt, how to measure it, and how and when to pay it down. Broadening the conventional definition of technical debt, they cover requirements debt, implementation debt, testing debt, architecture debt, documentation debt, deployment debt, and social debt. They intersperse technical discussions with "Voice of the Practitioner" sidebars that detail real-world experiences with a variety of technical debt issues.
Pro .NET Best Practices
Pro .NET Best Practices is a practical reference to the best practices that you can apply to your .NET projects today. You will learn standards, techniques, and conventions that are sharply focused, realistic and helpful for achieving results, steering clear of unproven, idealistic, and impractical recommendations. Pro .NET Best Practices covers a broad range of practices and principles that development experts agree are the right ways to develop software, which includes continuous integration, automated testing, automated deployment, and code analysis. Whether the solution is from a free and open source or a commercial offering, you will learn how to get a continuous integration server running and executing builds every time code changes. You will write clearer and more maintainable automated testing code that focuses on prevention and helping your .NET project succeed. By learning and following the .NET best practices in this book, you will avoid making the same mistakes once. With this book at your side, you'll get: Real-world, no-nonsense approaches to continuous integration, automated testing, automated deployment, and code analysis Tips and tricks you'll need to clear hurdles that keep others from putting these common sense ideas into common practice Guidance from the minimal, essential approach all the way to what's necessary to deliver at the highest levels of quality and effectiveness Benefit immediately, even before finishing it, from the knowledge, workable advice, and experience found in Pro .NET Best Practices.
Machine Learning in Production
A practical and innovative textbook detailing how to build real-world software products with machine learning components, not just models. Traditional machine learning texts focus on how to train and evaluate the machine learning model, while MLOps books focus on how to streamline model development and deployment. But neither focus on how to build actual products that deliver value to users. This practical textbook, by contrast, details how to responsibly build products with machine learning components, covering the entire development lifecycle from requirements and design to quality assurance and operations. Machine Learning in Production brings an engineering mindset to the challenge of building systems that are usable, reliable, scalable, and safe within the context of real-world conditions of uncertainty, incomplete information, and resource constraints. Based on the author’s popular class at Carnegie Mellon, this pioneering book integrates foundational knowledge in software engineering and machine learning to provide the holistic view needed to create not only prototype models but production-ready systems. • Integrates coverage of cutting-edge research, existing tools, and real-world applications • Provides students and professionals with an engineering view for production-ready machine learning systems • Proven in the classroom • Offers supplemental resources including slides, videos, exams, and further readings