AI in software engineering at Google: Progress and the path aheadPhilosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more
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Google OCS Apollo: The >$3 Billion Game-Changer in Datacenter Networking
Networking is a critical part of any datacenter, especially with the rise of networking-intensive large language models. As such, it was a clear target for Google’s infrastructure optimization efforts. Over the last year at conferences such as OFC and SIGCOMM, Google disclosed their custom networking stack, Jupiter, from in-house switches all the way through to custom reconfigurable software. This
GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.
Currently, there is an astonishing amount of toil and guesswork involved in actually getting deep neural networks to work well in practice. Even worse, the actual recipes people use to get good results with deep learning are rarely documented. Papers gloss over the process that led to their final results in order to present a cleaner story, and machine learning engineers working on commercial prob
Go Style
Documents The Style Guide outlines the foundation of Go style at Google. This document is definitive and is used as the basis for the recommendations in Style Decisions and Best Practices. Style Decisions is a more verbose document that summarizes decisions on specific style points and discusses the reasoning behind the decisions where appropriate. These decisions may occasionally change based on
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API Improvement Proposals
Welcome AIPs are design documents that summarize Google's API design decisions. They also provide a framework and system for others to document their own API design rules and practices. AIPs are a combination of design guidance and a system we use to manage and track that guidance. Learn more about how the AIP program works in the first AIP!
GitHub - jhuangtw/xg2xg: by ex-googlers, for ex-googlers - a lookup table of similar tech & services
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ãŠã²ã•ã—ã¶ã‚Šã§ã™ã€‚ 今日ã¯è‡ªåˆ†ã®ãƒ¡ãƒ¢ã§ã™ãŒã“ã‚Œã‹ã‚‰èªã¿ãŸã„è«–æ–‡ã¨ãã®ç†ç”±ã«ã¤ã„ã¦è¨˜ã—ã¦ãŠãã¾ã™ã€‚ã¡ãªã¿ã«å…¨ã¦Google関連ã§ã™ã€‚開発ã«é–¢ã‚ã‚‹ã‚‚ã®ã‚‚ã‚ã‚Œã°ã€ã‚¤ãƒ³ãƒ•ãƒ©åŸºç›¤ã®ã‚‚ã®ã‚‚ã‚ã‚Šã¾ã™ã€‚ 1. Advantages and Disadvantages of a Monolithic Codebase research.google Googleã§ã¯ç‹¬è‡ªã®VCS(ãƒãƒ¼ã‚¸ãƒ§ãƒ³ç®¡ç†ã‚·ã‚¹ãƒ†ãƒ )ãŒã‚ã£ã¦ã€ãã‚ŒãŒãƒ¢ãƒŽãƒ¬ãƒã§ç®¡ç†ã•ã‚Œã¦ã„ã‚‹ã¨ã„ã†è©±ã‚’èžã„ãŸã“ã¨ãŒã‚ã‚Šã¾ã™ã€‚ 大é‡ã®Google Internalãªã‚½ãƒ•ãƒˆã‚¦ã‚§ã‚¢ãŒã‚ã‚‹ãªã‹ã§ãれらをã©ã†ã‚„ã£ã¦ã¾ã¨ã‚‚ã«ç®¡ç†ã—ã¦ã‚‹ã®ã‹ã¯ãšã£ã¨æ°—ã«ãªã£ã¦ã„ãŸã®ã§ã€ã“れをèªã‚“ã らãªã«ã‹ã‚ã‹ã‚Šãã†ã¨æ€ã„ã¾ã—ãŸã€‚ 2. Modern Code Review: A Case Study at Google research.google コードレビューã®ãƒ—ãƒã‚»ã‚¹ã«ãŠã„ã¦ã€ãƒ¢ãƒ€
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Advancing Multimodal Medical Capabilities of Gemini
Microsoftã‚„Amazonã«ç¶šãã€Googleも生æˆAIå‘ã‘独自CPU投入
GitHub - google/comprehensive-rust: This is the Rust course used by the Android team at Google. It provides you the material to quickly teach Rust.
https://ai.google/static/documents/palm2techreport.pdf
Large-scale cluster management at {Google} with {Borg}
SRE for ML: The First 10 Years and the Next 10 | USENIX
An Overview of Google's Work on AutoML and Future Directions
MediaPipe Iris: Real-time Iris Tracking & Depth Estimation
GitHub - google/tensorstore: Library for reading and writing large multi-dimensional arrays.
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