# classification Published articles for classification. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs DevFeed: [Glyph: A Multi-Strategy Agentic System for Column Description and Sensitivity-Ontology Tagging of Enterprise Data Catalogs]() Original publisher: [Read original article]() Published: 2026-09-16T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [data](), [Large Language Model](), [Code](), [Retrieval-Augmented Generation](), [Access Control](), [Graphs](), [GitHub]() Tags: [agents](), [classification](), [code](), [data](), [documentation](), [governance](), [production](), [provenance](), [retrieval](), [vector-database]() ### AI overview Glyph is a production system for generating column descriptions and assigning sensitivity-ontology labels in enterprise data catalogs. It uses cooperating LLM agents, source-code-grounded retrieval, parallel tagging strategies, vector-based metadata matching, and ranked-output fusion to support auditable cataloging. ### Source excerpt Enterprise data lakes accumulate tables faster than human stewards can document or classify them, leaving columns with missing descriptions and unassigned governance labels. This documentation debt undermines data discovery, access control, and regulatory compliance. We present Glyph, a production system that frames two coupled problems, column description generation and column type annotation for data classification, as cooperating LLM agents orchestrated as stateful graphs. The Descriptor grounds generation in the pipeline source code that produces each column, retrieved on demand from an... ## Teaching AI to Reason Through Detection Triage DevFeed: [Teaching AI to Reason Through Detection Triage]() Original publisher: [Read original article]() Author: Amol Khanna - Manu Nandan - Cristian Viorel Popa - Joan Pujol-Roig - Diana Bolocan - Laura Vasilie - Alexandru Apostu - Chase Helwig - Mihaela Gaman - Mickey Brautbar - Edward Raff - Chase Midler - Sv Published: 2026-09-12T11:17:51.295154Z Content type: article Language: en Sources: [Blog]() Topics: [AI Chat]() Tags: [agentic](), [agentic-soc](), [ai](), [automation](), [chain-of-thought](), [classification](), [cybersecurity](), [language-models](), [llm](), [model](), [nemotron](), [nvidia](), [open](), [reasoning](), [research](), [security](), [soc](), [windows]() ### AI overview CrowdStrike describes research on a reasoning-enabled language-model classifier for security detection triage. The model produces a verdict and an auditable rationale, with the stated goals of improving accuracy, transparency, and safe alert automation. ### Source excerpt New CrowdStrike research shows how step-by-step reasoning can improve detection triage accuracy, transparency, and safe automation. ## AI Functions in ClickHouse: Upgrade your SQL to the AI age DevFeed: [AI Functions in ClickHouse: Upgrade your SQL to the AI age]() Original publisher: [Read original article]() Author: Andriy Yakovlev; George Larionov Published: 2026-09-11T12:49:32Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [Large Language Model](), [AI search](), [Retrieval Augmented Generation (RAG)](), [Databases]() Tags: [ai](), [classification](), [clickhouse](), [embeddings](), [llm](), [rag](), [sql]() ### AI overview ClickHouse introduces beta AI Functions that invoke LLM and embedding providers directly from SQL for tasks including classification, extraction, generation, translation, filtering, redaction, embeddings, and semantic similarity. ### Source excerpt Explore ClickHouse AI Functions for classification, generation, translation, embeddings, semantic search, and cost controls--all directly from SQL. ## Building Jarvis Pro: Route first, answer later DevFeed: [Building Jarvis Pro: Route first, answer later]() Original publisher: [Read original article]() Author: Christian Coffrant Published: 2026-08-21T00:00:00Z Content type: article Language: en Sources: [Grab Tech]() Topics: [AI Chat]() Tags: [account-management](), [ai](), [artificial-intelligence](), [business](), [classification](), [data](), [engineering](), [evaluation](), [llm](), [product](), [routing]() ### AI overview Grab describes Jarvis Pro, an AI assistant for account managers that routes a request to a constrained task type before generating an answer. The design aims to avoid confident but unsuitable merchant recommendations, and reports offline routing and answer-quality evaluation results. ### Source excerpt Introduction The first Jarvis Pro prototype could produce answers that sounded right. That was the problem. One early answer looked polished: it named the merchant, summarized the week, and recommended pushing promotions before the next review. It was also wrong. The merchant's order volume was down, but the sharper issue was operational: more outlets were paused and fulfilment had slipped. Sending more demand into that setup would have made the merchant look worse. That failure changed how we judged the system. Fluent was not enough. Jarvis Pro is the AI assistant we built for Grab account managers. Its job is to help them turn account data into better merchant conversations: what changed, why it changed, and what to do next. They rarely ask clean dashboard questions. They ask: "I am meeting this merchant tomorrow. What should I tell them?" or "Which accounts in my portfolio need attention this week?" Those questions hide decisions: scope, access, business diagnosis, and metric definition. If the system gets those wrong, confidence becomes a liability. So the core design became: route first, answer later. In an internal offline evaluation (not a measure of production performance or business impact), routing matched the expected safe route for 99.4% of 351 realistic prompts drawn from labelled eval sets from the first half of 2026. In a focused portfolio and brand answer-quality suite, the average score moved from 78.5 to 91.0. These figures come from offline launch-readiness evaluation only; they are not business-impact proof. Why dashboards were easier A dashboard answers a bounded question: "Show net sales for merchant X last week." An account review question has to diagnose the work to be done: "This merchant softened this week. Should I push promos, ads, or operations fixes before the review?" If outlets were paused, more traffic can backfire. If average order value fell, the next action may be menu or bundle design. If a dashboard, warehouse table, and local s ## How we think about text classification in the LLM era DevFeed: [How we think about text classification in the LLM era]() Original publisher: [Read original article]() Author: Raphael Montaud Published: 2026-08-19T20:00:37Z Content type: article Language: en Sources: [Medium]() Topics: [Machine Learning & Artificial Intelligence](), [Model Development](), [LLM Techniques](), [Artificial Intelligence](), [AI Inference]() Tags: [ai](), [classification](), [inference](), [llm](), [llms](), [machine-learning](), [model](), [models](), [recommendation-system](), [text-classification]() ### AI overview Medium explains how it is evaluating LLM-based text classification for updating its aging NSFW model while retaining task-specific machine-learning models. The article states that Snowflake LLM tools were used for inference only and that Medium's user data was not used to train the models. ### Source excerpt Why we think LLMs can be useful and why we will not replace all of our models with themContext At Medium, we have many Machine Learning models that we use to label stories automatically. These affect what stories we recommend to readers. Here's some examples: a few of our text classification models. All diagrams and charts made by the authorSome Clarifications on our Machine Learning policy Before we go deep on this project, I just wanted to clarify a few things about how we stand regarding AI in general. Medium has been training internal models with user and post data for a long time now. We train models with specific tasks. For example, models that power our recommendations algorithm, or text classification models like the ones presented in this story. All in the goal to improve our product. With the LLM approach I describe in this story, we ARE NOT sharing these models with other companies. And we ARE NOT allowing anyone to train on our users' data and content. Here we used Snowflake LLM tools for inference only (no LLM training was done here) and they are actually hosting all of the models inside their own infrastructure and guarantee that they are not using any of this for training. Shoutout to the Snowflake team for making it so easy and safe to use LLMs on our data! If you want to read more about Medium's stance on AI, I definitely recommend giving these a read: Default No to AI Training on Your Stories Finally, an internet standard for writers' rights vs. AI companies We want your feedback: How can writers use AI to tell human stories? Problem During our roadmap planning we decided that our NSFW model was out of date and it was time to revamp it. This model labels stories as "Not Safe for Work" if they have sexually explicit content, lots of profanity, or basically anything you wouldn't want to read on your big monitor in the middle of an open space! As you can imagine it's a pretty important model. We really need it to make sure our most "interesting" conte ## Empty shelves or lost keys? Recall is the bottleneck for parametric factuality DevFeed: [Empty shelves or lost keys? Recall is the bottleneck for parametric factuality]() Original publisher: [Read original article]() Published: 2026-08-12T09:51:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Google](), [LLM evaluation / benchmarking](), [Large Language Model](), [Benchmark](), [Hallucination detection](), [AI Inference]() Tags: [benchmark](), [classification](), [errors](), [generative-ai](), [google](), [inference](), [large-language-models-llms](), [natural-language-processing](), [post-training](), [research]() ### AI overview This Google Research article argues that many factual errors in frontier large language models arise from recall failures rather than missing encoded knowledge. It presents knowledge profiling, which separates encoding, recall, and recognition, and introduces WikiProfile, a benchmark of 2,150 Wikipedia-derived facts tested through questions targeting these abilities. ### Source excerpt Generative AI ## Calibrating LLM-Based Population Estimates with Human Validation DevFeed: [Calibrating LLM-Based Population Estimates with Human Validation]() Original publisher: [Read original article]() Author: Hiroshi Urata Published: 2026-08-12T00:29:34Z Content type: article Language: en Sources: [Indeed]() Topics: [Large Language Model](), [human feedback](), [data](), [LLM evaluation / benchmarking]() Tags: [classification](), [data](), [data-science](), [dataset](), [evaluation](), [false-negative](), [false-positive](), [llm](), [machine-learning](), [measurement](), [validation]() ### AI overview The article explains how human validation can calibrate LLM-based population estimates. It distinguishes an LLM's apparent positive rate from true prevalence, uses human-validated samples to estimate sensitivity and specificity, and applies those error estimates to correct population-level measurements and quantify uncertainty. ### Source excerpt Key Idea Human validation is not only for evaluating an LLM. It can also calibrate how the LLM is used as a scalable measurement instrument for population estimation. An LLM can classify thousands of records at low cost, but the proportion it classifies as positive is not necessarily the true proportion in the population. By [...] ## Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard DevFeed: [Route AI Agent Workloads Across Models with NVIDIA NeMo Switchyard]() Original publisher: [Read original article]() Author: Michelle Horton Published: 2026-08-11T13:00:00Z Content type: tutorial Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [AI Bots]() Tags: [agent](), [agentic](), [agentic-ai-generative-ai](), [agents](), [ai](), [ai-agent](), [applications](), [benchmark](), [classification](), [computer-use](), [cost](), [deepseek](), [developer-tools-techniques](), [developers](), [engineering](), [featured](), [latency](), [llms](), [math](), [model](), [models](), [nemo](), [nvidia](), [reasoning](), [top-stories]() ### AI overview The article explains how NVIDIA NeMo Switchyard routes AI-agent tasks to different models according to task requirements, capabilities, cost, and latency. ### Source excerpt Building an AI agent does not end with choosing a single model. Each model has its own strengths, weaknesses, and cost profile, which can shift from one... ## High Risk Merchant Account: Costs, Options, Alternatives DevFeed: [High Risk Merchant Account: Costs, Options, Alternatives]() Original publisher: [Read original article]() Author: Deepak Jangir Published: 2026-08-05T00:00:00Z Content type: article Language: en Sources: [Dodo Payments Blog]() Topics: [Software as a service]() Tags: [alternatives](), [article](), [business](), [classification](), [compliance](), [cost](), [payment](), [payment-processing](), [payments](), [pricing](), [saas]() ### AI overview This article explains why payment providers classify businesses as high risk, focusing on dispute history, regulatory exposure, delivery gaps, advance payment, and recurring billing. It discusses the costs of high risk merchant accounts, including specialist pricing and rolling reserves that can constrain working capital, and introduces alternatives for digital sellers. ### Source excerpt Why payment providers classify a business as high risk, what a high risk merchant account actually costs, and which alternatives work for digital sellers. ## Introducing TabFM: A zero-shot foundation model for tabular data DevFeed: [Introducing TabFM: A zero-shot foundation model for tabular data]() Original publisher: [Read original article]() Published: 2026-06-30T10:26:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [foundation-models](), [datasets](), [Machine learning](), [Google](), [Feature Engineering](), [Hyperparameter optimization](), [Language models](), [BigQuery](), [GitHub](), [hugging face](), [AI Inference]() Tags: [bigquery](), [classification](), [data](), [data-management](), [feature-engineering](), [github](), [google](), [hugging-face](), [hyperparameter-optimization](), [machine-intelligence](), [model](), [product](), [zero-shot]() ### AI overview Google Research introduces TabFM, a zero-shot foundation model for tabular-data classification and regression. It frames prediction as in-context learning, reducing the need for dataset-specific training, hyperparameter optimization, and feature engineering, with availability through Hugging Face, GitHub, and BigQuery. ### Source excerpt Data Management ## AI-generated synthetic neurons speed up brain mapping DevFeed: [AI-generated synthetic neurons speed up brain mapping]() Original publisher: [Read original article]() Published: 2026-04-16T12:18:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [AI Models](), [Google](), [Point cloud](), [Training AI Models](), [datasets](), [neuron](), [data]() Tags: [3d](), [accelerate](), [ai](), [classification](), [errors](), [general-science](), [generation](), [google](), [health-bioscience](), [iclr](), [iclr-2026](), [images](), [machine-intelligence](), [mapping](), [model](), [models](), [neuron](), [partners](), [research](), [scale](), [science](), [synthetic](), [visualization]() ### AI overview Google Research describes how MoGen generates synthetic neuronal shapes to improve AI models that reconstruct brain wiring maps. Adding synthetic training examples reduced reconstruction errors by 4.4%, potentially saving 157 person-years of manual proofreading for a complete mouse brain. ### Source excerpt General Science ## Introducing GPT-5.4 mini and nano DevFeed: [Introducing GPT-5.4 mini and nano]() Original publisher: [Read original article]() Published: 2026-03-17T10:00:00Z Content type: release Language: en Sources: [OpenAI News]() Topics: [AI-assisted coding](), [AI Bots](), [debugging]() Tags: [agent](), [benchmarks](), [classification](), [codex](), [coding](), [company](), [cost](), [debugging](), [gpt](), [latency](), [multimodal](), [reasoning](), [tool]() ### AI overview OpenAI introduces GPT-5.4 mini and nano, smaller models positioned for fast, high-volume coding, reasoning, multimodal, and tool-use workloads. The article emphasizes lower latency, cost efficiency, coding workflows, and use as supporting subagents. ### Source excerpt GPT-5.4 mini and nano are smaller, faster versions of GPT-5.4 optimized for coding, tool use, multimodal reasoning, and high-volume API and sub-agent workloads. ## Introducing GIST: The next stage in smart sampling DevFeed: [Introducing GIST: The next stage in smart sampling]() Original publisher: [Read original article]() Published: 2026-01-23T17:46:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Algorithms](), [Algorithms & Theory](), [datasets](), [data](), [Machine Learning & Artificial Intelligence](), [Algorithm](), [Google](), [NeurIPS]() Tags: [algorithms](), [algorithms-theory](), [classification](), [computer-vision](), [data](), [data-mining-modeling](), [datasets](), [diversity](), [embedding](), [google](), [language-models](), [large-language-models-llms](), [machine-intelligence](), [machine-learning](), [neurips](), [points](), [research](), [systems](), [training](), [vision]() ### AI overview Google Research introduces GIST, an algorithm for selecting a high-quality subset of data for model training. It balances diversity, which reduces redundancy, with utility, which favors relevant and informative data, and provides a mathematical guarantee about solution quality. ### Source excerpt Algorithms & Theory ## Segmented models for personal income estimation at Mercado Libre DevFeed: [Segmented models for personal income estimation at Mercado Libre]() Original publisher: [Read original article]() Author: Samuel Vazquez Published: 2026-01-08T20:44:23Z Content type: article Language: en Sources: [Mercado Libre Tech]() Topics: [data](), [personalization](), [datasets](), [big-data]() Tags: [big-data](), [business](), [classification](), [data](), [dataset](), [estimations](), [personal-income](), [personalization](), [regression]() ### AI overview Mercado Libre describes a "silver shotgun" approach for heterogeneous real-world data: segmenting data across contexts, training specialized models for each segment, and combining their predictions. The article presents personal income estimation as an application supporting more personalized financial products and discusses how geographic scale changes the importance of features such as home GPS location. ### Source excerpt Before we dive in, let's give a huge shout-out to Julio Avila Torreblanca and Alejandro Hernandez Cuevas for their exceptional work in developing this system. Thanks also to Enrique Miranda for reviewing the text and providing valuable feedback. Introduction Real-world data is rarely homogeneous. The same feature can mean different things depending on the city, neighborhood, urban density, or user segment. When we force a single global model to explain all those regimes, we usually pay with lower performance. At Mercado Libre, we take a different approach: instead of looking for a "silver bullet," we use a "silver shotgun." This pattern segments the data in several ways, trains specialized models per segment, and combines their predictions into a single estimate. Motivation: Financial inclusion is a significant opportunity In Mexico, only 63% of adults have a formal bank account, only 37% have a credit product, and less than 16% have a bank credit card [1]. About 45% of employed people have a formal source of income, while the rest work in the informal sector [2]. This gap presents a great opportunity for Mercado Libre to enter a growing market. Hyper-personalization is key to our mission to democratize financial inclusion across Latin America (LatAM). To offer great credit products, such as credit cards, we need a reliable estimate of a person's income. Big data, big problems Imagine we want to build a personal income estimation model for people visiting a very small local park. Every day, our dataset would grow by a few hundred entries for children playing, people exercising, and dog walkers, among other activities. Because people in the park live just a few blocks away, home location would have a limited effect (same neighborhood means low GPS variance). The features with the biggest impact on estimated income would likely be age and occupation. If we scale the problem to an entire city, home GPS location will have a bigger effect on the estimate. Each neighborho ## From Waveforms to Wisdom: The New Benchmark for Auditory Intelligence DevFeed: [From Waveforms to Wisdom: The New Benchmark for Auditory Intelligence]() Original publisher: [Read original article]() Published: 2025-12-03T22:47:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Benchmark](), [Machine Intelligence](), [datasets](), [multimodal](), [hugging face](), [Open Source](), [Google](), [NeurIPS]() Tags: [2025](), [ai](), [benchmark](), [classification](), [data](), [datasets](), [embedding](), [google](), [hugging-face](), [machine-intelligence](), [multimodal](), [neurips](), [open-source](), [retrieval](), [sound-accoustics](), [speech-processing]() ### AI overview Google Research introduces the Massive Sound Embedding Benchmark (MSEB), an open-source benchmark for evaluating machine sound intelligence across eight capabilities, including transcription, classification, retrieval, reasoning, segmentation, clustering, reranking, and reconstruction. It also includes the Simple Voice Questions dataset, with 177,352 spoken queries across 26 locales and 17 languages, available on Hugging Face. ### Source excerpt Machine Intelligence ## MedGemma: Our most capable open models for health AI development DevFeed: [MedGemma: Our most capable open models for health AI development]() Original publisher: [Read original article]() Published: 2025-10-25T18:02:50Z Content type: article Language: en Sources: [Google DeepMind News]() Topics: [AI Development](), [Artificial Intelligence](), [multimodal](), [gemma](), [gemma4](), [Application Development](), [GPU](), [Hardware](), [Google]() Tags: [2025](), [ai](), [ai-development](), [classification](), [development](), [gemma](), [generation](), [google](), [gpu](), [hardware](), [health](), [healthcare](), [image](), [mobile](), [models](), [multimodal](), [open](), [product-development](), [research](), [retrieval]() ### AI overview Google Research announces two open models in the MedGemma collection for health AI development: MedGemma 27B Multimodal and MedSigLIP. The models support medical text and imaging tasks, including electronic health record interpretation, report generation, visual question answering, classification, and retrieval. ### Source excerpt We're announcing new multimodal models in the MedGemma collection, our most capable open models for health AI development. ## Beyond classification: How AI agents are evolving Shopify's product taxonomy at scale DevFeed: [Beyond classification: How AI agents are evolving Shopify's product taxonomy at scale]() Original publisher: [Read original article]() Author: Kshetrajna Raghavan Published: 2025-10-09T05:18:20Z Content type: article Language: en Sources: [Shopify Engineering](), [Shopify Engineering - Shopify Engineering]() Topics: [Shopify](), [Artificial Intelligence](), [vlm]() Tags: [agents](), [ai](), [ai-agents](), [classification](), [predictions](), [products](), [shopify]() ### AI overview Shopify describes an AI multi-agent system that evolves its product taxonomy alongside changing commerce. The system is intended to improve more than product classification by updating taxonomy labels across more than 10,000 categories and 2,000 attributes, addressing scale, domain expertise, and consistency challenges. ### Source excerpt Last year, over 875 million people bought items from Shopify merchants. Building on our prior Vision Language Model-based product classification, this post explores how AI agents are evolving the taxonomy itself. ## Offline Evaluation of Open-Source Embedding Models for Model Selection DevFeed: [Offline Evaluation of Open-Source Embedding Models for Model Selection]() Original publisher: [Read original article]() Author: Tiago Cabo Published: 2025-10-07T15:11:01Z Content type: article Language: en Sources: [OLX]() Topics: [Embeddings](), [Benchmark](), [Open Source](), [recommendation systems](), [hugging face](), [AI Models]() Tags: [ai](), [classification](), [embedding](), [embeddings](), [evaluation](), [machine-learning](), [open-source](), [recommendations](), [search]() ### AI overview This article explains how OLX evaluated open-source embedding models against its existing internal model. It describes embeddings, the MTEB benchmark, and practical constraints such as memory, latency, modality, and multilingual support when selecting a model for deployment. ### Source excerpt The Importance of Offline Evaluation to Guide Model ChoiceIntroduction Recent advancements in open-source AI models make it challenging to justify the development of custom models, given the high quality of existing options. This also applies to embedding models, which are available in impressive quality. At OLX, we utilize a model called Item2vec to generate similar item recommendations. For more details, please refer to our blog post. Item2Vec: Neural Item Embeddings to enhance recommendations In this work, we developed an embedding model that not only improved recommendations but was also used by other teams across OLX, such as the search team. For an in-depth look at this application, see our post on Hybrid Search, where we tried to balance the benefits of semantic and lexical search. Hybrid Search -- Where Keywords Meet Vectors, Enabling Classifieds Discovery This article discusses our evaluation of open-source embedding models compared to our existing internal model. Open Source Embeddings An embedding model converts inputs, such as text or images, into vectors. These vectors are then used to compute similarities, often cosine distance, where closer vectors are deemed semantically related. Cosine similarity Many architectures types for embedding models support this, including word2vec, GloVe, RNN, LSTM, and transformers. This leads to hundreds of publicly available models, making selection a challenge. Common benchmarks play a crucial role in this process. After exploring various options, we chose the MTEB: Massive Text Embedding Benchmark. We selected this benchmark due to its widespread community adoption and extensive features, such as: 56 datasets across 8 tasks Support for up to 112 different languages Easy extensibility via the repo MTEB HuggingfaceBenchmark MTEB Hugging Face provides a comprehensive leaderboard showcasing top-performing models across tasks (retrieval, classification, etc), reflecting advancements in machine learning and AI. The higher, t ## Training and Finetuning Sparse Embedding Models with Sentence Transformers DevFeed: [Training and Finetuning Sparse Embedding Models with Sentence Transformers]() Original publisher: [Read original article]() Author: Tom Aarsen; Arthur BRESNU Published: 2025-07-01T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [sentence-transformers](), [Embeddings](), [hugging face](), [Benchmark]() Tags: [benchmark](), [bm25](), [classification](), [community](), [datasets](), [embedding](), [examples](), [guide](), [hugging-face](), [models](), [nlp](), [open-source](), [search](), [sentence-transformers](), [training](), [transformers](), [vector]() ### AI overview This tutorial explains how to fine-tune sparse embedding models with Sentence Transformers. It covers the required components, pretrained sparse encoders available through the Hugging Face Hub, the distinction between dense and sparse embeddings, interpretability through vocabulary tokens, and neural query/document expansion compared with BM25. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Getting Started with Error Diagnostics in GraphOS Studio DevFeed: [Getting Started with Error Diagnostics in GraphOS Studio]() Original publisher: [Read original article]() Author: Tim Hingston Published: 2025-06-24T08:52:53Z Content type: release Language: en Sources: [Apollo Blog]() Topics: [GraphQL](), [GraphOS](), [API](), [Apollo Connectors](), [Back end]() Tags: [apollo-connectors](), [apollo-federation](), [classification](), [errors](), [graphos](), [graphql](), [schema](), [security](), [server](), [tooling]() ### AI overview Apollo announces Extended Error Diagnostics for GraphOS Studio, a framework and set of tools for identifying, classifying, and addressing GraphQL errors across federated graphs. Errors can be categorized by type, code, and originating service. ### Source excerpt Apollo Federation makes it easy to orchestrate your backend services into a single unified API. With the recently announced availability of Apollo Connectors, even existing REST APIs can now be easily federated together. Today, we're proud to announce Extended Error Diagnostics, a set of tools designed to help developers quickly identify, classify, and address errors across their graph. HTTP vs. ## Microsoft and Hugging Face expand collaboration DevFeed: [Microsoft and Hugging Face expand collaboration]() Original publisher: [Read original article]() Author: Jeff Boudier; Simon Pagezy; Alvaro Bartolome Published: 2025-05-19T00:00:00Z Content type: news Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [vulnerability]() Tags: [announcement](), [artificial-intelligence](), [audio](), [azure](), [classification](), [collaboration](), [deployment](), [hugging-face](), [images](), [microsoft](), [model](), [models](), [open](), [open-source](), [partnership](), [security](), [text-generation](), [text-to-image](), [vulnerability]() ### AI overview Microsoft and Hugging Face expanded their collaboration to make more than 10,000 Hugging Face models available through Azure AI Foundry. The collection supports deployment of models for text, audio, and image tasks, with listed security screening requirements. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## 1 Billion Classifications DevFeed: [1 Billion Classifications]() Original publisher: [Read original article]() Author: Derek Thomas Published: 2025-02-13T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Inference](), [inference-endpoints](), [model-deployment](), [k6](), [Latency](), [Embeddings](), [Grafana](), [Hardware](), [Testing](), [hugging face](), [JavaScript](), [.env]() Tags: [classification](), [cost](), [cpu](), [embedding](), [embeddings](), [enterprise](), [grafana](), [inference](), [inference-endpoints](), [javascript](), [k6](), [latency](), [nlp](), [nvidia](), [performance](), [python](), [scale](), [testing]() ### AI overview This article presents a methodology for calculating cost and latency when running large-scale classification and embedding workloads. It examines model architectures, hardware options, deployment, load testing, and inference servers, with a focus on processing 1 billion inputs and balancing batch-inference cost against latency. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Timm ❤ Transformers: Use any timm model with transformers DevFeed: [Timm ❤ Transformers: Use any timm model with transformers]() Original publisher: [Read original article]() Author: Aritra Roy Gosthipaty; Ross Wightman; Pavel Iakubovskii; Pedro Cuenca; Vaibhav Srivastav Published: 2025-01-16T00:00:00Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [AI, ML & Data Engineering](), [object-detection](), [web applications]() Tags: [api](), [classification](), [community](), [computer-vision](), [cv](), [fine-tuning](), [gradio](), [integration](), [pytorch](), [quantization](), [timm](), [transformers]() ### AI overview A tutorial on integrating timm computer-vision models with Transformers for pipelines, quantization, fine-tuning, and inference, including an image-classification app example. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. ## Category Prediction for Search Query Understanding DevFeed: [Category Prediction for Search Query Understanding]() Original publisher: [Read original article]() Author: music and waves Published: 2024-04-21T12:40:48Z Content type: article Language: en Sources: [Myntra]() Topics: [Query (disambiguation)](), [Training AI Models](), [datasets](), [data]() Tags: [ai](), [classification](), [data](), [deep-learning](), [ecommerce](), [information-retrieval](), [model](), [neural](), [nlp](), [search](), [text-classification](), [train](), [training]() ### AI overview This article describes Myntra's multi-label product-category classification model for understanding ambiguous search queries. It covers preparing query-and-category training data and training a neural text classifier to predict relevant categories for live searches. ### Source excerpt Navigating through online shopping platforms can sometimes feel like finding your way through a maze. Take the search bar, for example. You type in "winter upper wear," hoping to find the perfect jacket or cozy sweatshirt. But here's the tricky part: the search engine has to decipher what you mean. Is it jackets you're after? Or maybe sweatshirts? Or both? It gets even more confusing when you consider the overlapping categories. Kurtas can be standalone articles or part of kurta sets. And loafers? They could belong to formal shoes or casual shoes and certainly not sports shoes. See the challenge? To tackle this, Myntra uses a multi-label search to product category classification model. It's like having an assistant that can understand possible intents from your search query. So when you type in something like "whey," the model knows you might be looking for protein or health supplements. But here's the catch: search queries can be short and vague, and they often use words that don't directly match category names. People might search using different terms or even regional variations. So, the model needs to be clever enough to map those words to the right categories internally. The goal is to capture all possible intents without cluttering your search results with irrelevant stuff. After all, nobody likes sifting through pages of irrelevant products. It's a delicate balance between covering all bases and keeping things tidy. Solution The solution has 2 major components. I. Data preparation We prepare ( search query : categories ) data points to be consumed in training by the neural classifier. Ex. ( ethnic wear : kurta, sarees ) II. Training a neural model We train a neural multi-label text classifier that consumes the prepared training data which is used to predicts categories for search queries live. I. Data Preparation We generate the supervised text classification training data in form of a search query and its product category(s) as its labels. This set is enrich [Next page]()