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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.
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Amazon Announces Immediate Availability of Asia Pacific (Mumbai) Region
On June 27th, Amazon announced the immediate availability of their 6th AWS Region in Asia Pacific. This region is in Mumbai, India and it joins other regions in Asia Pacific including Beijing, Seoul, Singapore, Sydney, and Tokyo. With the addition of Mumbai, Amazon is now up to 35 Availability Zones across 13 geographic Regions worldwide.
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Uber Unveils its Realtime Market Platform
Matt Ranney, Chief Systems Architect at Uber, gave an overview of their dispatch system, responsible for matching Uber's drivers and riders. Ranney explained the driving forces that led to a rewrite of this system. He described the architectural principles that underpin it, several of the algorithms implemented and why Uber decided to design and implement their own RPC protocol.