Eric Colson

An interactive tour of some of the algorithms in use at Stitch Fix.

What do you call a practice that most data scientists have heard of, few have tried, and even fewer know how to do well? It turns out, no one is quite certain what to call it. In our latest free report Real-World Active Learning: Applications and Strategies for Human-in-the-Loop Machine Learning, we examine the relatively new field of “active learning” — also referred to as “human computation,” “human-machine hybrid systems,” and “human-in-the-loop machine learning.” Whatever you call it, the… Show more

What do you call a practice that most data scientists have heard of, few have tried, and even fewer know how to do well? It turns out, no one is quite certain what to call it. In our latest free report Real-World Active Learning: Applications and Strategies for Human-in-the-Loop Machine Learning, we examine the relatively new field of “active learning” — also referred to as “human computation,” “human-machine hybrid systems,” and “human-in-the-loop machine learning.” Whatever you call it, the field is exploding with practical applications that are proving the efficiency of combining human and machine intelligence. Show less

For years now we’ve espoused data-driven decision making into the organization. And, while we still need to take this further, there is equal opportunity in internalizing the “art” that exists within the organization. The judgement and cultural values that reside within the brains of our employees can be harvested and married with data science to produce new capabilities. In this talk we will share new ideas about how to systematically combine the assets of the organization – be they machines… Show more

For years now we’ve espoused data-driven decision making into the organization. And, while we still need to take this further, there is equal opportunity in internalizing the “art” that exists within the organization. The judgement and cultural values that reside within the brains of our employees can be harvested and married with data science to produce new capabilities. In this talk we will share new ideas about how to systematically combine the assets of the organization – be they machines or humans. Show less

Machine algorithms are great for tasks that require processing of large amounts of objective and structured data. However, they have difficulty with tasks that are relatively simple for skilled humans – For example, interpreting concepts in an image, or discerning tone in language, ..etc. Yet, there is a class of problems that call for precisely the combination of these tasks. This concept of human-assisted algorithmic processing is not new. It is inherent to many processes that we are familiar… Show more

Machine algorithms are great for tasks that require processing of large amounts of objective and structured data. However, they have difficulty with tasks that are relatively simple for skilled humans – For example, interpreting concepts in an image, or discerning tone in language, ..etc. Yet, there is a class of problems that call for precisely the combination of these tasks. This concept of human-assisted algorithmic processing is not new. It is inherent to many processes that we are familiar with. However, there are very few systems that embrace humans and machines as two resources within a single system. Instead, they are often independent and non-collaborating agents. In this talk, we explain how a single task-processing system can be architected to use diverse resources: be they human or machine. Such a system not only better utilizes each resource, but also produces better results and gets better with experience. Show less

The traditional cycle in the fashion industry starts on the runway, with designs that are finally available for consumer purchase several months later. But as this O’Reilly report shows, consumers are now an integral part of the full fashion cycle—even before some styles come to fruition—as fashion innovators find new ways to bring data analytics to the industry.

Through interviews with several fashion startups, authors Liza Kindred and Julie Steele reveal that these pioneers are talking… Show more

The traditional cycle in the fashion industry starts on the runway, with designs that are finally available for consumer purchase several months later. But as this O’Reilly report shows, consumers are now an integral part of the full fashion cycle—even before some styles come to fruition—as fashion innovators find new ways to bring data analytics to the industry.

Through interviews with several fashion startups, authors Liza Kindred and Julie Steele reveal that these pioneers are talking to customers and getting valuable data from them, in ways that other industries would be wise to emulate. Some aspects of fashion are becoming more agile, as startups such as Poshly, Rent the Runway, and Stitch Fix respond to customer input on sizing, preference, and more.

As you’ll discover, data science has already made big alterations to the $3 trillion fashion industry, via a growing number of fashion data tools and trends, including custom fit and local manufacturing. At the same time, there is lots of room for exploration and innovation, especially in the areas of machine vs human insight, image processing, and online vs offline data collection.

Liza Kindred is the founder of Third Wave Fashion, a fashion tech think tank, and the author of the upcoming O’Reilly book How We Buy Now.

Julie Steele is Director of Communications at Silicon Valley Data Science and coauthor of Beautiful Visualization and Designing Data Visualizations (both O’Reilly). Show less

Developing recommendation algorithms so accurate that we can commit to them.