# ML Applications Published articles for ML Applications. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## An Organizational Second Brain: Building an AI That Learns From Experts DevFeed: [An Organizational Second Brain: Building an AI That Learns From Experts]() Original publisher: [Read original article]() Author: Shaurya Sengar; Jason Nawrocki; Jay Shah; Prashant Kommireddi Published: 2026-09-02T09:00:29Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research](), [Meta ML Applications]() Topics: [Large Language Model](), [Architecture & Design]() Tags: [ai-agent](), [ai-research](), [architecture](), [compliance](), [llms](), [ml-applications](), [security-privacy]() ### AI overview Meta describes an AI agent for a compliance domain that preserves specialist knowledge through an auditable knowledge architecture, an expert-like reasoning layer, and a feedback-driven improvement pipeline without model retraining. ### Source excerpt We've built an AI agent that acts as a secondary expert for a given domain, making deep specialist knowledge readily available and preserved for anyone in an organization to access, share, and build upon. This is not a typical domain-specific agent. Its novelty comes from integrating two layers: A structured, auditable knowledge architecture separates what [...] Read More... The post An Organizational Second Brain: Building an AI That Learns From Experts appeared first on Engineering at Meta. ## From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking DevFeed: [From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking]() Original publisher: [Read original article]() Author: Steven De Gryze; Parshva Doshi; Sean O'Byrne; Arnold Overwijk; Dinesh Ramasamy; Lee Xiong Published: 2026-08-05T19:20:20Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta ML Applications]() Topics: [recommendation systems](), [Temporal data]() Tags: [ads](), [architecture](), [data-infrastructure](), [meta](), [ml-applications](), [production-engineering](), [recommendation-systems](), [scaling-laws](), [tokenization]() ### AI overview Meta describes a multi-stage sequence-model architecture for ads ranking that separates offline user modeling from lightweight online ranking. It also uses dense tokenization and target-aware attention to learn feature interactions, with reported conversion and ad-click lifts across Instagram and Facebook. ### Source excerpt Every day, Meta's recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content. In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) [...] Read More... The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta's Ads Ranking appeared first on Engineering at Meta. ## GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model DevFeed: [GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model]() Original publisher: [Read original article]() Author: Darren Liu; Huayu Li; Raghav Boinepalli; Yuzhen Huang; Jackie (Jiaqi) Xu; Richard Qiu; Chunzhi Yang; Rich Zhu; Dev (Devashish) Shankar; Huaqing Xiong Published: 2026-08-03T18:00:17Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta AI Research](), [Meta ML Applications]() Topics: [recommendation systems](), [networking]() Tags: [ads](), [ai-infrastructure](), [ai-research](), [data-infrastructure](), [gpu](), [hardware](), [kernels](), [llm](), [meta](), [ml-applications](), [networking](), [recommendation-systems](), [training]() ### AI overview Meta describes training its GEM ads recommendation foundation model at LLM scale. The article covers recommendation-specific kernels, ultra-low-precision training, and topology-aware parallelism that doubled end-to-end training efficiency to 20-25% MFU while increasing training FLOPs fourfold. ### Source excerpt Meta's Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs. This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20-25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in [...] Read More... The post GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model appeared first on Engineering at Meta. ## Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler DevFeed: [Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler]() Original publisher: [Read original article]() Author: Blaise Sanouillet; Sunyi Shao; Jeff Song; Gosh Arzumanyan; Shawn Wu; Tejun Heo; Praveen Sampath; GP Musumeci Published: 2026-07-13T16:00:50Z Content type: article Language: en Sources: [Engineering at Meta](), [Meta ML Applications]() Topics: [Latency](), [Linux](), [Optimization](), [Open Source](), [Google]() Tags: [engineering](), [kernel](), [latency](), [linux](), [meta](), [ml-applications](), [open-source](), [optimization](), [performance]() ### AI overview Meta describes using the open-source, BPF-based sched_ext framework to create a Linux kernel scheduling policy tailored to its ads delivery workload. The approach addressed latency regressions associated with a kernel upgrade and produced lower tail latency, reduced power consumption, and a modest increase in ads ranked. ### Source excerpt TL; DR At Meta's scale, a few milliseconds of latency degradation can have a significant negative impact on ads performance. When a Linux kernel upgrade risked regressing latency across Meta's ad serving fleet, we turned to sched_ext -- the upstream, BPF-based extensible scheduling framework -- to build a scheduling policy customized to the Ads delivery [...] Read More... The post Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler appeared first on Engineering at Meta. ## 10 Years of Meta's Commitment to Python DevFeed: [10 Years of Meta's Commitment to Python]() Original publisher: [Read original article]() Author: Chris Wiltz Published: 2026-06-30T16:00:46Z Content type: opinion Language: en Sources: [Meta AI Research](), [Meta ML Applications]() Topics: [Meta](), [Python](), [Open Source](), [Programming language](), [Software](), [PyTorch]() Tags: [ai-research](), [culture](), [data-infrastructure](), [devinfra](), [engineering](), [meta](), [ml-applications](), [open-source](), [production-engineering](), [programming](), [python](), [pytorch](), [software]() ### AI overview Meta reflects on its 10th consecutive year sponsoring the Python Software Foundation and explains Python's importance across its engineering stack, including products, infrastructure, and AI research. ### Source excerpt This year marks Meta's 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it. Python is one of the world's most influential programming languages, and we use it across our engineering stack, from [...] Read More... The post 10 Years of Meta's Commitment to Python appeared first on Engineering at Meta. ## Privacy-Aware Infrastructure in the AI-Native Era: An Asset Classification Case Study DevFeed: [Privacy-Aware Infrastructure in the AI-Native Era: An Asset Classification Case Study]() Original publisher: [Read original article]() Author: Rituraj Kirti; Vasileios Lakafosis Published: 2026-06-25T22:30:51Z Content type: article Language: en Sources: [Meta ML Applications]() Topics: [data](), [Large Language Model](), [Artificial Intelligence](), [audit](), [systems](), [Meta]() Tags: [ai](), [audit](), [data](), [engineering](), [meta](), [ml-applications](), [review](), [security-privacy](), [systems]() ### AI overview This Meta engineering article presents a hybrid approach to asset classification for privacy-aware infrastructure. It combines rich context, LLMs for ambiguous or novel assets, human-reviewed labels, and deterministic versioned rules for routine production enforcement. ### Source excerpt Privacy controls -- systems that enforce retention, access, allowed-purpose, downstream-sharing, or anonymization policies -- require a reliable understanding of data to function. Before such a control can operate effectively, it must know exactly what it is looking at. This can be complex, as demonstrated by a field simply named "age": In one context, it [...] Read More... The post Privacy-Aware Infrastructure in the AI-Native Era: An Asset Classification Case Study appeared first on Engineering at Meta. ## SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems DevFeed: [SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems]() Original publisher: [Read original article]() Author: Lei Chen; Yiyi Pan; Ivy Sun; Sha Meng; Cornelia Carapcea; Shilin Ding; Ram Ramanathan; Nipun Mathur; Hong Yan; Lars Backstrom Published: 2026-05-26T16:00:01Z Content type: article Language: en Sources: [Meta ML Applications]() Topics: [recommendation systems](), [Neural Network](), [Architecture & Design](), [systems]() Tags: [compute](), [efficiency](), [engineering](), [meta](), [ml-applications](), [model](), [recommendation-systems](), [research](), [retrieval](), [systems]() ### AI overview Meta introduces SilverTorch, a unified model-based retrieval system for recommendation systems. It replaces separate retrieval microservices with modules in a single neural network, allowing more complex modeling and more candidates to be evaluated within a sub-100-millisecond target. The article reports up to 23.7x higher throughput and 20.9x greater compute cost efficiency than specified baseline approaches. ### Source excerpt We're introducing SilverTorch, a reimagining of recommendation systems that unifies all retrieval components for user generated content under a unified architecture. SilverTorch shows up to 23.7x higher throughput compared to the state-of-the-art approaches. It's also showing 20.9x more compute cost efficiency compared to a CPU-based solution while also improving accuracy. Our research paper, "SilverTorch: A [...] Read More... The post SilverTorch: Index as Model -- A New Retrieval Paradigm for Recommendation Systems appeared first on Engineering at Meta. ## Meta Tech Podcast: Building the Friend Bubbles Feature for Facebook Reels DevFeed: [Meta Tech Podcast: Building the Friend Bubbles Feature for Facebook Reels]() Original publisher: [Read original article]() Author: Pascal Hartig Published: 2026-05-13T13:00:44Z Content type: article Language: en Sources: [Meta ML Applications]() Topics: [Meta](), [Machine learning]() Tags: [android](), [culture](), [engineering](), [ios](), [meta](), [meta-tech-podcast](), [ml-applications](), [podcast]() ### AI overview A Meta Tech Podcast episode discusses how Facebook Reels engineers built Friend Bubbles, a feature that highlights Reels watched and reacted to by a user's friends. The episode covers the machine learning model behind the feature and differences between iOS and Android user behavior. ### Source excerpt On its face the new Friend Bubbles feature looks simple enough. It highlights Reels your friends have watched and reacted to. But sometimes the features that seem the most straightforward require the deepest engineering work. On this episode of the Meta Tech Podcast, Pascal Hartig chats with Subasree and Joseph, two software engineers from the Facebook [...] Read More... The post Reel Friends: Building Social Discovery that Scales to Billions appeared first on Engineering at Meta. ## Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge DevFeed: [Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge]() Original publisher: [Read original article]() Author: Shubhojeet Sarkar; Shengbo Guo; Guohao Zhang; Woon Jo; Laura Vig Published: 2026-04-21T16:00:19Z Content type: article Language: en Sources: [Meta ML Applications]() Topics: [Meta](), [systems]() Tags: [architecture](), [automated](), [consensus](), [discovery](), [engineering](), [facebook](), [improvements](), [meta](), [ml-applications](), [model](), [retrieval](), [search](), [validation]() ### AI overview Meta describes a modernization of Facebook Groups Search using hybrid retrieval and automated model-based evaluation. The article explains how the changes address discovery, content consumption, and validation challenges in community search, while reporting improvements in engagement and relevance without increased error rates. ### Source excerpt We've fundamentally transformed Facebook Groups Search to help people more reliably discover, sort through, and validate community content that's most relevant to them. We've adopted a new hybrid retrieval architecture and implemented automated model-based evaluation to address the major friction points people experience when searching community content. Under this new framework, we've made tangible improvements [...] Read More... The post Modernizing the Facebook Groups Search to Unlock the Power of Community Knowledge appeared first on Engineering at Meta. ## RCCLX: Innovating GPU Communications on AMD Platforms DevFeed: [RCCLX: Innovating GPU Communications on AMD Platforms]() Original publisher: [Read original article]() Author: Sudharssun Subramanian; Subodh Iyengar; Cen Zhao; Srinath Bayareddy; James Hongyi Zeng Published: 2026-02-24T21:30:54Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [GPU](), [Meta](), [communications](), [AI Models](), [Latency](), [Inference](), [Large Language Model](), [Transformer]() Tags: [accelerate](), [ai](), [ai-models](), [ai-research](), [communications](), [data-center-engineering](), [gpu](), [inference](), [large-language-model](), [latency](), [layer](), [meta](), [ml-applications](), [networking-traffic](), [open-source](), [parallelism]() ### AI overview Meta describes the initial open-source release of RCCLX, an enhanced version of RCCL for AMD platforms integrated with Torchcomms. The article presents Direct Data Access algorithms and Low Precision Collectives, including approaches intended to reduce communication latency during large language model inference. ### Source excerpt We are open-sourcing the initial version of RCCLX - an enhanced version of RCCL that we developed and tested on Meta's internal workloads. RCCLX is fully integrated with Torchcomms and aims to empower researchers and developers to accelerate innovation, regardless of their chosen backend. Communication patterns for AI models are constantly evolving, as are hardware [...] Read More... The post RCCLX: Innovating GPU Communications on AMD Platforms appeared first on Engineering at Meta. ## Meta's ACH Tool Uses LLMs for Mutation-Guided Test Generation and Compliance Testing DevFeed: [Meta's ACH Tool Uses LLMs for Mutation-Guided Test Generation and Compliance Testing]() Original publisher: [Read original article]() Author: Mark Harman Published: 2025-09-30T16:00:08Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [Testing](), [mutation-testing](), [Large Language Model](), [Meta](), [Artificial Intelligence](), [Software]() Tags: [ai](), [ai-research](), [code](), [llms](), [meta](), [ml-applications](), [mutation-testing](), [security-privacy](), [testing]() ### AI overview Meta describes its Automated Compliance Hardening (ACH) tool, which uses large language models to generate relevant code mutants and tests designed to catch them. The article explains how this supports mutation testing and helps identify compliance-related bugs. ### Source excerpt Following our keynote presentations at FSE 2025 and Eurostar 2025, we're delving further into the development of Meta's Automated Compliance Hardening (ACH) tool, an LLM-based tool for software testing that is automating aspects of compliance adherence at Meta, while accelerating developer and product velocity. By leveraging LLMs we've been able to overcome the barriers that [...] Read More... The post LLMs Are the Key to Mutation Testing and Better Compliance appeared first on Engineering at Meta. ## Meta 3D AssetGen: Generating 3D Worlds With AI DevFeed: [Meta 3D AssetGen: Generating 3D Worlds With AI]() Original publisher: [Read original article]() Author: Pascal Hartig Published: 2025-09-29T14:00:42Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [3D](), [Artificial Intelligence](), [foundation-models](), [Large Language Model](), [Meta]() Tags: [3d](), [ai](), [ai-research](), [foundation](), [images](), [keynote](), [learning](), [llms](), [meta](), [meta-tech-podcast](), [ml-applications](), [model](), [podcast](), [text](), [virtual](), [virtual-reality](), [worlds]() ### AI overview A Meta Tech Podcast episode discusses AssetGen, a foundation model for 3D assets. Meta's XR Tech team explains how it was built and trained, the role of LLMs in VR, and efforts to generate complete 3D worlds from text prompts. ### Source excerpt Imagine being able to use AI to create 3D virtual worlds using prompts as easily as you can generate images. The intersection of AI and VR was one of the biggest topics at Meta Connect this year. In his keynote, Mark Zuckerberg shared his vision of a future where anyone can create virtual worlds using [...] Read More... The post Meta 3D AssetGen: Generating 3D Worlds With AI appeared first on Engineering at Meta. ## Meta's Infrastructure Evolution and the Advent of AI DevFeed: [Meta's Infrastructure Evolution and the Advent of AI]() Original publisher: [Read original article]() Author: Yee Jiun Song; Kaushik Veeraraghavan Published: 2025-09-29T13:00:15Z Content type: article Language: en Sources: [Meta AI Research]() Topics: [Meta](), [Artificial Intelligence](), [Hardware](), [Network](), [cloud-infrastructure](), [Terraform]() Tags: [ai](), [ai-research](), [apache](), [data-center-engineering](), [data-centers](), [data-infrastructure](), [data-management](), [database](), [devinfra](), [hardware](), [infrastructure](), [lamp](), [linux](), [meta](), [ml-applications](), [mysql](), [network](), [networking-traffic](), [open-source](), [production-engineering](), [scale]() ### AI overview Meta describes how its infrastructure evolved from a small university-focused social network into a globally networked operation serving more than 3.4 billion people. The article explains that AI has changed infrastructure-scaling assumptions and requires innovation across hardware, software, networks, and data centers, while outlining earlier database, caching, social graph, ranking, and photo-service scaling work. ### Source excerpt Over the past 21 years, Meta has grown exponentially from a small social network connecting a few thousand people in a handful of universities in the U.S. into several apps and novel hardware products that serve over 3.4 billion people throughout the world. Our infrastructure has evolved significantly over the years, growing from a [...] Read More... The post Meta's Infrastructure Evolution and the Advent of AI appeared first on Engineering at Meta. ## Product planning for machine learning DevFeed: [Product planning for machine learning]() Original publisher: [Read original article]() Author: Juan Hernandez Published: 2017-10-19T17:46:52Z Content type: tutorial Language: en Sources: [Square Corner Blog RSS Feed]() Topics: [Machine learning](), [AI Development]() Tags: [automation](), [data-science](), [machine-learning](), [ml-applications](), [optimization]() ### AI overview This article explains how to plan machine-learning-based solutions by considering the operational environment, business context, product strategy, and customer needs alongside the model itself. It distinguishes strategic ML applications for insights and recommendations from tactical applications that automate or optimize processes. ### Source excerpt What you should know in order to plan machine-learning-based solutions.