David Tan – författare

Fashion is a multi-billion-dollar global business, which is not surprising because of our basic need to wear clothes and shoes. The fashion system thrives on ephemerality, novelty, seduction and hedonism. There are countless books on intellectual property law, numerous books on fashion theory, and a few books on fashion law, but hardly any on fashion and intellectual property. This book assembles a constellation of some of the best-known intellectual property scholars around the world to present their analysis of how different aspects of intellectual property laws interact with and regulate the fashion industry. It presents a meticulously curated collection of how intellectual property laws interact with contemporary fashion and culture studies in protecting fashion creations that range from clothing and footwear to textiles. It covers key features of intellectual property rights regimes in the United States, United Kingdom, Europe, Australia, and Asia that include copyright, trademarks, patents and geographical indications. This title is also available as open access on Cambridge Core.

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team''s flow when building ML systems and products.

Based on the authors'' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You''ll learn how to overcome friction and experience flow when delivering ML solutions.

You''ll also learn how to:

Write automated tests for ML systems, containerize development environments, and refactor problematic codebasesApply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutionsApply Lean delivery and product practices to improve your odds of building the right product for your usersIdentify suitable team structures and intra- and inter-team collaboration techniques to enable fast flow, reduce cognitive load, and scale ML within your organization

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists, ML engineers, and their leaders will learn how to bridge the gap between data science and Lean product delivery in a practical and simple way. David Tan, Ada Leung, and Dave Colls show you how to apply time-tested software engineering skills and Lean product delivery practices to reduce toil and waste, shorten feedback loops, and improve your team''s flow when building ML systems and products.

Based on the authors'' experience across multiple real-world data and ML projects, the proven techniques in this book will help your team avoid common traps in the ML world, so you can iterate and scale more quickly and reliably. You''ll learn how to overcome friction and experience flow when delivering ML solutions.

You''ll also learn how to:

Write automated tests for ML systems, containerize development environments, and refactor problematic codebasesApply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutionsApply Lean delivery and product practices to improve your odds of building the right product for your usersIdentify suitable team structures and intra- and inter-team collaboration techniques to enable fast flow, reduce cognitive load, and scale ML within your organization

Gain the valuable skills and techniques you need to accelerate the delivery of machine learning solutions. With this practical guide, data scientists and ML engineers will learn how to bridge the gap between data science and Lean software delivery in a practical and simple way. David Tan and Ada Leung from Thoughtworks show you how to apply time-tested software engineering skills and Lean delivery practices that will improve your effectiveness in ML projects.Based on the authors' experience across multiple real-world data and ML projects, the proven techniques in this book will help teams avoid common traps in the ML world, so you can iterate more quickly and reliably. With these techniques, data scientists and ML engineers can overcome friction and experience flow when delivering machine learning solutions.This book shows you how to:Apply engineering practices such as writing automated tests, containerizing development environments, and refactoring problematic code basesApply MLOps and CI/CD practices to accelerate experimentation cycles and improve reliability of ML solutionsDesign maintainable and evolvable ML solutions that allow you to respond to changes in an agile fashionApply delivery and product practices to iteratively improve your odds of building the right product for your usersUse intelligent code editor features to code more effectively

Celebrities can sell anything from cars to clothing, and we are constantly fascinated by their influence over our lifestyle choices. This book makes an important contribution to legal scholarship about the laws governing the commercial appropriation of fame. Exploring the right of publicity in the US and the passing off action in the UK and Australia, David Tan demonstrates how an appreciation of the production, circulation and consumption of fame can be incorporated into a pragmatic framework to further the understanding of the laws protecting the commercial value of the celebrity personality. Using contemporary examples such as social media and appropriation art, Tan shows how present challenges for the law may be addressed using this cultural framework. This book will be of interest to intellectual property law academics, judges, practitioners and students in the US and common law jurisdictions, as well as those in the field of cultural studies.