How Uber Uses YAML

Uber’s Rate Limiting System details the evolution of Uber's approach to managing service overload through a unified rate-limiting architecture.

This article discusses how Uber has integrated explainability into its machine learning platform, Michelangelo, using Integrated Gradients (IG) to provide interpretable attributions for deep learni...

The article discusses the open-source release of Starlark Worker, a tool that integrates Cadence workflow orchestration with the Starlark scripting language.

The article discusses Uber's implementation of a configuration-driven archival and retrieval framework designed to manage vast amounts of regulatory data efficiently.

The article discusses Uber's approach to continuous deployment (CD) for large monorepos, detailing the challenges faced and the solutions implemented to automate deployments safely and efficiently.

The article discusses the Sparkle framework developed by Uber to standardize modular ETL processes, enhancing developer productivity and data quality.

The article discusses Uber's DataK9 platform, which automates the categorization of vast amounts of data at the field level using AI/ML techniques.

The article discusses the management of Network Intrusion Detection System (IDS) rulesets at Uber using Aristotle v2, focusing on automating the process of filtering and enhancing rules to reduce f...

The article discusses Uber's implementation of Attribute-Based Access Control (ABAC) to manage access across its microservices architecture.

The article discusses how Uber implemented an incremental ETL process using Apache Hudi to manage its transactional data lake.

This article discusses how Uber unified its configuration distribution across various systems through the Unified Configuration Distribution Platform (UCDP).

The article discusses SLATE, a system developed by Uber to simplify developer testing through the creation of on-demand, ephemeral testing environments.

The article discusses uBuild, Uber's platform for building container images efficiently and securely.

The article discusses Uber's next-generation infrastructure stack, named Crane, which aims to modernize and scale Uber's infrastructure for a hybrid, multi-cloud environment.

The article discusses the development of the YAML Generator application at Uber, designed to streamline the creation of funnel YAML files for mobile data workflows.

The article discusses the architecture of Uber's API gateway, detailing its evolution, components, and functionalities.

The article discusses Uber's journey towards establishing a better data culture by addressing critical data issues and implementing a holistic approach to data management.

Ludwig v0. 2 introduces significant enhancements to its deep learning toolbox, including new features such as Comet.

The article introduces the Plato Research Dialogue System, a flexible conversational AI platform developed by Uber AI.

The article introduces the Autonomous Visualization System (AVS), an open standard developed by Uber for visualizing data from autonomous vehicles.

The article introduces Ludwig, an open-source deep learning toolbox developed by Uber that allows users to train and test deep learning models without writing code.

AresDB is Uber's GPU-powered open-source real-time analytics engine designed to enhance data processing capabilities.

This article discusses the design and implementation of Schemaless, Uber Engineering's scalable datastore built on MySQL.