The diagram below shows how we think about the standard ML development process and the different feedback loops within them. Jeremy Hermann is an engineering manager on Uber's Michelangelo team.
Originally, our platform and infrastructure components were combined into a single system. To build an internal community, we host an annual internal ML conference called UberML. The first version of these models, based on boosted trees, sped up ticket handling time by 10 percent with similar or better customer satisfaction. Plus, features of pooling makes riding more economical via decreasing cost for riders.
Again, Michelangelo’s feature store is critical to enabling teams to reuse important predictive features already identified and built by other teams. Because of this, model developer velocity is critically important. Uber leverages ML in several ways for making exceptional customer experience and seamless Uber’s services. : Providing end-to-end workflows is important for handling the most common ML use cases, but to address the less common and more specialized cases, it’s important to have primitive components that can be assembled in targeted ways. We do this by democratizing the tools and support our technical teams need, namely, optimizing for developer velocity, end-to-end ownership, software engineering rigor, and system flexibility.
This means more accurate models in the same amount of training time or less training time to get to a high-quality model. In addition to training simple models, users can compose more complex transformation pipelines, ensembles, and stacked models. The faster we go, the more experiments we can run, the more hypotheses we can test, the better results we can get. We found that the same workflow applies across a wide array of scenarios, including traditional ML and deep learning; supervised, unsupervised, and semi-supervised learning; online learning; batch, online, and mobile deployments; and time-series forecasting. Uber’s Map Services team developed a sophisticated segment-by-segment routing system that is used to calculate base ETA values.
Using Michelangelo, Uber Eats also estimates meal arrival times based on predicted ETAs, historical data, and various real-time signals for the meal and restaurant. We made some major changes to the Michelangelo architecture to leverage our existing systems as much as possible while evolving with growing requirements as ML matured across the company: We also found that for some problem domains, specialized development experiences are useful. For example, launching an automated update to an ETA prediction model that uses anonymized data requires less privacy scrutiny than launching a new pricing model.
Demand forecasting systems allows the app to hike the prices marginally midst peak hours that augment profit and demand ultimately. Our focus on community extends beyond our own walls.
Sharing ML best practices (e.g., data organization methods, experimentation, and deployment management) and instituting more structured processes (e.g., launch reviews) are valuable ways to guide teams and avoid repeating others’ mistakes. A Marketplace team creates specialized tools that sit on top of Michelangelo, making it easier to manage these Marketplace ML projects. One of the leading business companies in transportation is “Uber”, it uses tech that aims to defeat the challenges of increasing travelling demand, severe gases, safety concerns, and unavoidable environmental impacts. which gates how fast data scientists can iterate and get feedback on their new models or features either in an offline test or from an online experiment. -Seth Godin.
are heavily used to automate or speed-up large parts of the process of responding to and resolving these issues. We were able to address some of these issues through the bridge teams, as described above. How simple process is that in an Uber app, press a button, a car comes up, you go for a ride and reach your destination, and again press a button to pay the driver.
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