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During our inaugural Uber Technology Day, data scientist Eva Feng delivered a presentation on Uber’s experimentation platform (XP). In this article, sheand colleague Zhenyu Zhao detail how Uber engineered an XP capable of rolling out new features stably and quickly at scale.

The lifecycle of feature development for a mobile app is composed of identifying opportunities, prototyping, experimentation, launching, refining, and identifying opportunities again. Experimentation is a critical stage of the product lifecycle; it is the process of discovering and determining whether or not new features are successful. Given Uber’s hypergrowth, the goal of our XP is to ensure that new features roll out successfully and then return actionable analysis.

Uber’s XP is unique in the industry because we launch not just experiments but also feature releases on a nationwide and even global scale which can almost immediately improve how people ( and now food ) get around in the physical world.

Initially, this massive scale across both products and markets made it challenging to build an XP that multiple teams with varying programming backgrounds and preferences could understand and use. Today, many teams at Uber use the XP to deploy features for their products, including the rider, driver, and UberEATS apps.

In this article, we will discuss the challenges and opportunities faced when developing our XP’s two primary components—a staged rollout and an intelligent analysis tool—as well as our finished product’s achieved outcomes.

All the World is a Stage(d Rollout)

The first element of the experimentation lifecycle at Uber is a staged rollout, the process of deploying a feature first to a small portion of users and then gradually ramping up to stages with larger proportions. Eventually, we reach 100 percent of all users that fall under a target specification (for instance, geographic location, which can be as small as a district of a city or as large as the entire world).

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I will leave aside the occasional searcher for the “holy grail” of very long-lived storage media (a materials science undertaking) combined with very low-tech, easy to produce reading technology. These seem unlikely to have much genuine impact on digital preservation, as they are not currently anywhere near consistent with the kinds of data volumes and transfer rates that are needed to be useful; they are more appropriate for challenges like identifying long-lived high-level nuclear waste sites for millennia across a dark age following the fall of civilization, which doesn’t require much bandwidth. See Benford (1999).

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I am deeply indebted to Don Waters for stressing this key observation.

21. , for example Varol, (2017), though this is much more focused on “emitters” (, tweeters) rather than relatively passive and private recorders.

22. And perhaps requiring the active collaboration of some components of this ecosystem in order to deceive others, which is perhaps more easily done in the name of national security than stewardship, if it can be accomplished at all.

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. Note that there are some really tricky problems using this kind of synthetic population generation to generate cadres of simulated users, because the population attributes that the target system conditions on and cares about are often tightly held secrets.

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Other than the problem of getting various data providers like Axciom to attribute suitable characteristics to all the software agents, it is provocative to think about very smart witnessing software that does most of the work of creating and managing a population of robot witnesses, and even trying to figure out the demography of this population both initially and on an ongoing basis. This seems far out of reach today, but consider the recent work by Google where they took various single-user video games from the 1980s and connected them to a machine learning system that essentially could read the screen, manipulate a mouse, and recognize the score and when its game was over. Astoundingly, the system fairly rapidly discovered optimal strategies for how to play these games. See Mnih, (2015) and Bellemere, (2016).

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This is actually a very complex and nuanced issue. It’s not clear what the scope of IRB jurisdiction is: it may well be that simply archiving material is not within the purview of the typical IRB, particularly if this isn’t being funded by grant support. At the same time, researchers trying to use archived materials later may find themselves subject to IRB oversight and constraints.

26. Regional market coverage started in 1954, though it’s not clear how many regional markets were covered.

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Mentoring is another strategy for transformative professional, personal and organizational development. By creating a supportive culture, mentoring can provide the environment for transformative learning to occur. Through this experience mentoring becomes a transformative relationship in which individuals reconstruct possible selves. As a two-way process, mentoring is a learning tool for both the mentor as well as the person being mentored. affordable free shipping collections Francesco Russo laceup slingback pumps prices sale online KPMCQ

In a recent study, Karen Weller Swanson, applying theories of critical reflection, incorporating a critical incident model, and positing a learning partnership, designed a program for practicing teachers, for the purpose of transformative teacher development. Experiences were created to get teachers to reflect on their assumptions, asking them to consider alternative perspectives and to develop a language for making connections between theory and practice. Over the studies' two-year period, teachers were able to develop ownership of theory and transfer this knowledge into their practice. To be effective, transformative teacher development must value what teachers bring from their personal and professional experience, and acknowledge that learning is both an individual and a social experience. The process involved: achieving transparency through negotiation of curriculum, finding a common language through discussion of individual assumptions, getting continuous feedback through critical-incident questions, and testing by teacher application of the program within their classrooms. Autobiography and journaling were additional techniques used within this learning partnership. Both teachers and faculty participating in the program were transformed as beliefs were challenged and knowledge was co-constructed throughout the experience. [45]





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