InfoQ Homepage Uber Content on InfoQ
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Data Collection, Standardization and Usage at Scale in the Uber Rider App
Uber Engineering recently published how it collects, standardises and uses data from the Uber Rider app. Rider data comprises all the rider's interactions with the Uber app. This data accounts for billions of events from Uber's online systems every day. Uber uses this data to deal with top problem areas such as increasing funnel conversion, user engagement, etc.
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Uber Re-Architected Its Foundational Fulfilment Service
Uber recently shared how it re-architected its fulfilment service, one of Uber's foundational platform services. Following a two-year-long effort involving 30+ teams and hundreds of developers, Uber engineers "built a strong foundation for modelling various types of physical fulfilment categories in the new platform and migrated all existing transportation use cases."
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InfoQ Live July 20th: Software Supply Chain for DevOps & Reducing Feature Flag Debt
How can modern DevOps practices accelerate your software delivery without the quality issues? Learn how automation, continuous testing, and supply management techniques can improve software quality and speed of delivery. Get valuable insights from world-class domain experts at InfoQ Live on July 20th.
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Uber Implements Disaster Recovery for Multi-Region Kafka
In a recent blog post, Uber engineers highlight how they use a replication platform to implement disaster recovery at scale with a multi-region Kafka deployment. Uber has a large deployment of Apache Kafka, processing trillions of messages and multiple petabytes of data per day. Uber's engineers provided business resilience and continuity in the face of natural and human-made disasters.
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Safe and Fast Deploys at Planet Scale: QCon Plus Q&A
Uber has automated the deployment of services using a hybrid cloud model. All services are deployed using the same rollout techniques and workflows, ensuring safe deployment and mitigation of any issues. Abstracting away the differences between clouds supports engineers in building services that run on any platform.
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Metrics Collection at Scale: Learning from Uber's M3
In a recent InfoQ podcast, Rob Skillington, co-founder and CTO at Chronosphere, shared his experience and opinions on the topic of observability in modern distributed systems. Key topics covered: metrics collection at scale, multi-dimensional metrics and high-cardinality, the importance of the developer experience, and the value of open standards, such as OpenMetrics.
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Uber Open-Sources AI Abstraction Layer Neuropod
Uber open-sourced Neuropod, an abstraction layer for machine learning frameworks that allows researchers to build models in the framework of their choice while reducing the effort of integration, allowing the same production system to swap out models implemented in different frameworks. Neuropod currently supports several frameworks, including TensorFlow, PyTorch, Keras, and TorchScript.
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How Uber Deals with Unreachable Code Associated to Feature Flags in its Mobile Apps
Piranha is a newly open-sourced tool by Uber that can be used to remove stale code in mobile apps written in Java, Objective-C, or Swift for Android and iOS. The tool was born with the aim to pay technical debt ensuing from the process of implementing and eventually removing feature flags, says Uber.
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Uber's Synthetic Training Data Speeds Up Deep Learning by 9x
Uber AI Labs has developed an algorithm called Generative Teaching Networks (GTN) that produces synthetic training data for neural networks which allows the networks to be trained faster than when using real data. Using this synthetic data, Uber sped up its neural architecture search (NAS) deep-learning optimization process by 9x.
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Uber Open-Sources Plug-and-Play Language Model for Controlling AI-Generated Text
Uber AI open-sourced the plug-and-play language model (PPLM) which can control the topic and sentiment of AI-generated text. The model's output is evaluated by human judges as achieving 36% better topic accuracy compared to the baseline GPT-2 model.
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Uber Releases Kraken: An Open Source P2P Docker Registry
Uber has released Kraken, an open source, peer-to-peer (P2P) Docker registry. Kraken is a highly available and scalable Docker registry tailored to meet the needs of enterprises and hybrid cloud environments.
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Uber Open-Sources Ludwig Code-Free Deep-Learning Toolkit
Uber Engineering is open-sourcing Ludwig, a deep-learning toolkit that allows users to experiment with a variety of neural network structures without writing code.
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Uber Introduces AresDB: GPU-Powered, Open-Source, Real-Time Analytics Engine
Uber recently introduced AresDB, an open-source real-time analytics engine leveraging an unconventional power source - graphics processing units (GPUs) - for meeting the growing demands of analysis at scale and at the same time unifying, simplifying and improving Uber’s existing solutions.
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Scaling Observability at Uber: Building In-House Solutions, uMonitor and Neris
Uber’s infrastructure consists of thousands of microservices supporting mobile applications, infrastructure, and internal services. To provide high observability of these services, Uber’s Observability team built two in-house monitoring solutions: uMonitor for time-series metrics-based alerting, and Neris for host-level checks and metrics.
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The Evolution of Uber’s 100+ Petabyte Big Data Platform
Uber’s engineering team wrote about how their big data platform evolved from traditional ETL jobs with relational databases to one based on Hadoop and Spark. A scalable ingestion model, standard transfer format and a custom library for incremental updates are the key components of the platform.