Kirjahaku

Cloud native development gives you the power to rapidly build, secure, and scale software. But you still need to navigate many potential pitfalls along the way. Through practical examples, this book demonstrates how to use Google Cloud as a laboratory to enable rapid innovation, a factory to automate build and testing, and a citadel to operate applications at scale securely. Author Daniel Vaughan shows you how to take applications from prototype to production by combining Google Cloud services, a cloud native programming model, and best practices. By following an example project from start to finish, developers, architects, and engineering managers working with the Google Cloud Platform will learn how to build and run cloud native applications on Google Cloud with confidence. With this book, you will: Understand cloud native development concepts including microservices, containerization, and event-driven architecture Learn Google Cloud services that specifically support this development style: compute, persistence, messaging, DevOps, security and networking, and observability Confidently build cloud native applications on Google Cloud Learn how to address nonfunctional requirements such as security, observability, and testing Successfully make the transition from initial proofs of concept and prototypes to production systems

This practical guide provides a collection of techniques and best practices that are generally overlooked in most data engineering and data science pedagogy. A common misconception is that great data scientists are experts in the "big themes" of the discipline-machine learning and programming. But most of the time, these tools can only take us so far. In practice, the smaller tools and skills really separate a great data scientist from a not-so-great one. Taken as a whole, the lessons in this book make the difference between an average data scientist candidate and a qualified data scientist working in the field. Author Daniel Vaughan has collected, extended, and used these skills to create value and train data scientists from different companies and industries. With this book, you will: Understand how data science creates value Deliver compelling narratives to sell your data science project Build a business case using unit economics principles Create new features for a ML model using storytelling Learn how to decompose KPIs Perform growth decompositions to find root causes for changes in a metric Daniel Vaughan is head of data at Clip, the leading paytech company in Mexico. He's the author of Analytical Skills for AI and Data Science (O'Reilly).

While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs. Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You'll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues. Break business decisions into stages that can be tackled using different skills from the analytical toolbox Identify and embrace uncertainty in decision making and protect against common human biases Customize optimal decisions to different customers using predictive and prescriptive methods and technologies Ask business questions that create high value through AI- and data-driven technologies