Résultats pour "Das System" dans Programmation et développement de logiciels

The system design interview is one of the hardest challenges you'll face in the software engineering hiring process. This practical book gives you the insights, the skills, and the hands-on practice you need to ace the toughest system design interview questions and land the job and salary you want.

Written by an expert who has created systems ranging from DNA scanners to children's toys, this book is ideal for intermediate and experienced programmers, no matter what platform you use. This expanded second edition includes new chapters on IoT and networked sensors, motors and movement, debugging, data handling strategies, and more.

Discover a comprehensive guide designed to equip you with the essential skills and knowledge needed to excel in the realm of Linux system administration. This book serves as a practical resource for individuals aspiring to become proficient (RHCSA) Red Hat Certified System Administrators, offering a structured approach to mastering the intricacies of Red Hat Enterprise Linux. You'll embark on a journey through the fundamental concepts and practical techniques essential for effectively managing Linux-based systems.

There are no easy decisions in software architecture. Instead, there are many hard parts—difficult problems or issues with no best practices—that force you to choose among various compromises. With this book, you'll learn how to think critically about the trade-offs involved with distributed architectures. Architecture veterans and practicing consultants Neal Ford, Mark Richards, Pramod Sadalage, and Zhamak Dehghani discuss strategies for choosing an appropriate architecture.

"Reliability Engineering in the Cloud: Strategies and Practices for AI-Powered Cloud-Based Systems" provides the roadmap to building systems that withstand failures, scale on demand, and deliver consistently exceptional experiences. Combining reliability engineering, AI-driven automation, and lean practices, this audiobook shows technology and business leaders how to turn cloud reliability into a strategic asset.

In this comprehensive guide, authors Kelly Shortridge and Aaron Rinehart help you navigate the challenges of sustaining resilience in complex software systems by using the principles and practices of security chaos engineering. By preparing for adverse events, you can ensure they don't disrupt your ability to innovate, move quickly, and achieve your engineering and business goals.

A decade ago, developers built information-sharing software by writing a lot of custom code in a single code base, then adding layers of caching. Now we build information systems: interdependent software and services, data platforms, and event streams. Many of our software-to-systems initiatives fail, though, and when they do, we double down on traditional linear approaches. But linear thinking cannot resolve systems challenges. We need to shift to systems thinking.

It is a fact that you'll be bombarded with system design interview questions which have become part and parcel of all the software engineering hiring processes. Your performance in these interviews will reflect upon your capability to work with complex systems and translate into the role and position that the interviewing organization is interviewing you for. This audiobook is a comprehensive guide to master all the concepts about SDIs.

Trust in Artificial Intelligence is not just a regulatory requirement—it is a matter of business ethics, cultural awareness, and responsible design. This book takes an integrated approach, revealing how Trustworthy AI Systems extend beyond algorithms, servers, and databases to also include appearance, environment, and humans.

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

Author Brendan Burns demonstrates how you can adapt existing software design patterns for designing and building reliable distributed applications. Systems engineers and application developers will learn how these long-established patterns provide a common language and framework for dramatically increasing the quality of your system. This fully updated second edition includes new chapters on AI inference, AI training, and building robust systems for the real world.

In our interconnected world, we face a complex cybersecurity ecosystem where digital vulnerabilities can have far-reaching consequences. Threats to digital infrastructure often impact critical physical systems, potentially causing real-world harm. With AI agents set to handle extensive personal information, data security and privacy are more crucial than ever.

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations.

To ensure that applications are reliable and always available, more businesses today are moving applications to AWS. But many companies still struggle to design and build these cloud applications effectively. With this practical guide, software, DevOps, and cloud engineers will learn how to implement resilient designs and configurations in the cloud using hands-on independent labs.

This book consists of 6 titles, namely: Assembly Language: The Ultimate Guide to Programming and Virtual Machines. Computer Science: The Complete Guide to Principles and Informatics. Cyber Security: Beginners’ Guide to Hacking, Phishing, Social Engineering, and Malware. Databases: System Concepts, Designs, Management, and Implementation. Operating Systems: Concepts to Save Money, Time, and Frustration. Website Development: Web Design Skills for Beginners

AI and other high-tech tools embed and reinforce America’s history of prejudice and exclusion — even when they are used with the best intentions. Patrick K. Lin’s Machine See, Machine Do: How Technology Mirrors Bias in Our Criminal Justice System takes a deep and thorough look into the use of technology in the criminal justice system, and investigates the instances of coded bias present at every level.

A guide to navigating the messiness of software development process change, dealing with all the legacy organizational elements, and focusing on increasing business agility.

Land that job! An in-depth overview of system design and how to prepare for your interview. When it comes to answering system design questions, many of us don't know where to start. We don't have the logic and knowledge to effectively communicate back a reply that impresses the interviewer. System Design Interview: A Strategic Guide for a Successful Interview is an easy-to-understand, step-by-step book that provides clarity on how to prepare and respond to questions in an interview.

Big Data teaches you to build big data systems using an architecture designed specifically to capture and analyze web-scale data. This book presents the Lambda Architecture, a scalable, easy-to-understand approach that can be built and run by a small team. You'll explore the theory of big data systems and how to implement them in practice. In addition to discovering a general framework for processing big data, you'll learn specific technologies like Hadoop, Storm, and NoSQL databases.