Showing results for "The Ascent" in Programming

As organizations shift from monolithic applications to smaller, self-contained microservices, distributed systems have become more fine-grained. But developing these new systems brings its own host of problems. This expanded second edition takes a holistic view of topics that you need to consider when building, managing, and scaling microservices architectures. Through clear examples and practical advice, author Sam Newman gives everyone from architects and developers to testers and IT operators a firm grounding in the concepts.

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 this insightful and comprehensive guide, Addy Osmani shares more than a decade of experience working on the Chrome team at Google, uncovering secrets to engineering effectiveness, efficiency, and team success. Engineers and engineering leaders looking to scale their effectiveness and drive transformative results within their teams and organizations will learn the essential principles, tips, and frameworks for building highly effective engineering teams.

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.

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.

Product management has become a critical function for modern organizations, from small startups to corporate enterprises. And yet, the day-to-day work of product management remains largely misunderstood. In theory, product managers are high-flying visionaries who build products that people love. In practice, they're hard-working facilitators who bring clarity and focus to their teams. In this thoroughly revised and expanded edition, Matt LeMay provides real-world guidance for current and aspiring product managers.

Salary surveys worldwide regularly place software architect in the top ten best jobs, yet no real guide exists to help developers become architects. Until now. This updated edition provides a comprehensive overview of software architecture's many aspects, with five new chapters covering the latest insights from the field. Aspiring and existing architects alike will examine architectural characteristics, architectural patterns, component determination, diagramming architecture, governance, data, generative AI, team topologies, and many other topics.

Designers and managers hope their products become essential for users-integrated into their lives like Instagram, Lyft, and others have become. Such deep integration isn't accidental: it's a process of careful design and iterative learning, especially for technology companies. This guide shows you how to apply behavioral science—research that supports many products—to help your users achieve their goals using your product.

Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology.

Drawing from her experience at Google and Meta, Dr. Marily Nika delivers the definitive guide for product managers building AI and GenAI powered products. Packed with smart strategies, actionable tools, and real-world examples, this book breaks down the complex world of AI agents and generative AI products into a playbook for driving innovation to help product leaders bridge the gap between niche AI and GenAI technologies and user pain points.

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.

How to Measure Anything in Project Management explains why popular methods for measurement in project management are flawed and describes how to conduct measurements that better inform decisions, reduce project risks, and improve the chance of project success.

Terraform has become a key player in the DevOps world for defining, launching, and managing infrastructure as code (IaC) across a variety of cloud and virtualization platforms, including AWS, Google Cloud, Azure, and more. This hands-on third edition, expanded and thoroughly updated for version 1.0 and beyond, shows you the fastest way to get up and running with Terraform.

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.

Kubernetes has become the dominant container orchestrator, but many organizations that have recently adopted this system are still struggling to run actual production workloads. In this practical book, four software engineers from VMware bring their shared experiences running Kubernetes in production and provide insight on key challenges and best practices.

With the recent generative AI boom, organizations are investing more resources into adapting artificial intelligence into their workloads, making the demand for AI skills higher than ever. With Microsoft Azure leading the pack with services for developing and deploying AI solutions, professionals looking to break into this field would be wise to start their journey by getting certified as an Azure AI Engineer. But Azure's AI-102 exam isn't a piece of cake.

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.

In The Accidental CIO, veteran software developer-turned-executive Scott Millett delivers an essential playbook to becoming an impactful, strategic leader at any stage of your IT leadership journey. You'll find a wealth of hands-on advice for tackling the many challenges and paradoxes that face technology leaders, from creating an aligned IT strategy, defining a target architecture, designing a balanced operating model, and leading teams and executing strategy.

After discussing what an algorithm does and how its effectiveness can be measured, Louridas covers three of the most fundamental applications areas: graphs, which describe networks, from eighteenth-century problems to today's social networks; searching, and how to find the fastest way to search; and sorting, and the importance of choosing the best algorithm for particular tasks. He then presents larger-scale applications: PageRank, Google's founding algorithm; and neural networks and deep learning.

We're at an inflection point in data, where our data management solutions no longer match the complexity of organizations, the proliferation of data sources, and the scope of our aspirations to get value from data with AI and analytics. In this practical book, Zhamak Dehghani introduces data mesh, a decentralized sociotechnical paradigm drawn from modern distributed architecture that provides a new approach to sourcing, sharing, accessing, and managing analytical data at scale.