# Model Development A machine-learning systems-engineering concept focused on creating and refining models for specific tasks and performance goals. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## App detects nearby Meta smart glasses as Meta develops camera-free glasses and launches paid social features DevFeed: [App detects nearby Meta smart glasses as Meta develops camera-free glasses and launches paid social features]() Original publisher: [Read original article]() Author: EditorDavid Published: 2026-09-17T11:34:00Z Content type: news Language: en Sources: [Slashdot]() Topics: [App](), [Meta](), [Bluetooth](), [iphone](), [Artificial Intelligence](), [data centers](), [Model Development](), [Users]() Tags: [ai](), [ai-data-centers](), [app](), [app-store](), [apple](), [camera](), [facebook](), [meta](), [model-development](), [social-media](), [subscriptions]() ### AI overview The article covers Zuckoff, an app that detects Bluetooth signals from nearby Meta smart glasses and estimates their distance. It also reports on Meta's planned camera-free glasses, the financial losses of Reality Labs, and paid subscription features for Instagram, Facebook, and WhatsApp. ### Source excerpt The New York Post reports: A new app called Zuckoff can detect if a user is near someone with Meta's creepy AI-powered glasses, which have drawn criticism for enabling creeps to record video of women without their consent. Programmer Pawel Szydlowski says tales of dirtbags recording themselves perving on women or harassing strangers prompted him to launch the app... Zuckoff -- which detects Bluetooth signatures broadcast by smart glasses and can estimate their distance -- has gained some 5,000 users since it hit Apple's App Store last month, according to Business Insider. "There's a need for such an application," the 30-year-old Polish techie told the outlet, adding that European Union regulators have contacted him for more info about his app. "We as a society have the right to at least know that someone is recording," Szydlowski said... Earlier this month, the tech giant disabled thousands of glasses it found had been tampered with to keep a small light off that indicates the device is recording. It's already #61 on the iPhone's list of best-selling utilities apps. In a related story, "After accusations of selling 'perv glasses,' Meta prepares to sell a pair without a camera," writes TechCrunch, citing a report from The Information. The glasses include six built-in microphones so users can communicate with the chatbot, as well as a button on the side of the glasses that, when pressed, activates the AI system... Meta's Reality Labs, which is responsible for developing its smart glasses line, is still losing a gargantuan amount of money, as its earnings report from April revealed. In fact, Meta stock is off 13% over the last 12 months, reports Yahoo Finance. So Tuesday Meta announced subscription services for its social apps as "part of Meta's push to drive additional revenue from the billions it's investing in AI data centers and model development." The plans, which start at $2.99 per month for single-product plans, $7.99 for individual bundles, and $14.99 for creator ## Announcing instance preference lists for Amazon SageMaker AI training jobs DevFeed: [Announcing instance preference lists for Amazon SageMaker AI training jobs]() Original publisher: [Read original article]() Author: Kanwaljit Khurmi Published: 2026-09-15T16:01:47Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [Amazon SageMaker AI](), [GPU](), [Model Development](), [data-processing]() Tags: [amazon-sagemaker-ai](), [announcements](), [gpu](), [intermediate-200](), [model-development](), [pipelines](), [scheduled](), [training]() ### AI overview Amazon SageMaker AI introduces instance preference lists for training and processing jobs. Users can specify up to five instance types in priority order, and SageMaker AI launches the job on the first option with available capacity, reducing manual retries and capacity monitoring. ### Source excerpt Amazon SageMaker AI now offers instance preference lists for training and processing jobs. Specify an ordered list of up to five instance types, and SageMaker AI automatically launches on the first type with available capacity, eliminating manual retry loops and capacity-watching scripts. ## Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it. DevFeed: [Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it.]() Original publisher: [Read original article]() Author: Meredith Shubel Published: 2026-09-10T19:37:30Z Content type: news Language: en Sources: [The New Stack]() Topics: [Model Development](), [Training AI Models](), [anthropic]() Tags: [ai](), [compute](), [infrastructure](), [models](), [news](), [open](), [training]() ### AI overview Mistral's EUR 3 billion Series D is presented as a bet that open-weight AI needs accompanying compute and infrastructure to reduce dependence on concentrated model and chip providers. ### Source excerpt This week, Mistral announced it raised EUR 3 billion in a Series D funding round, pushing its post-money valuation past EUR 21 The post Mistral wants open-weight AI to compete at the frontier. It just raised $3.5 billion to do it. appeared first on The New Stack. ## Transfer learning for genomic prediction in underrepresented populations DevFeed: [Transfer learning for genomic prediction in underrepresented populations]() Original publisher: [Read original article]() Published: 2026-09-03T18:20:31Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Fine-tuning](), [Model Development](), [Google](), [datasets]() Tags: [datasets](), [evaluation](), [general-science](), [google](), [healthcare](), [learning](), [machine-intelligence](), [research](), [training]() ### AI overview Google Research evaluates transfer learning for polygenic risk-score prediction across populations. European-cohort transfer learning improves prediction for small underrepresented target cohorts but can reduce accuracy as target cohorts grow, particularly for population-specific traits. ### Source excerpt General Science ## Agentic Machine Learning Modeling at Instacart DevFeed: [Agentic Machine Learning Modeling at Instacart]() Original publisher: [Read original article]() Author: Tilman Drerup Published: 2026-09-03T16:05:30Z Content type: article Language: en Sources: [Instacart]() Topics: [AI Agent](), [Model Development](), [AI Development](), [Machine learning](), [Development]() Tags: [agent](), [agentic](), [ai](), [ai-agent](), [artificial-intelligence](), [development](), [instacart](), [machine-learning](), [models](), [research]() ### AI overview Instacart describes how its machine learning engineers are exploring AI-agent-assisted modeling loops. The approach keeps engineers responsible for defining problems and supervising work while agents develop hypotheses, implement experiments, and evaluate them. ### Source excerpt Tilman Drerup, Moe Moazzami, Shih-Ting Lin, Greg Reda (and many more) Introduction At Instacart, artificial intelligence is fundamentally changing the way our machine learning engineers operate. In a prior blog post, we used one of our teams as a case study to illustrate how the emergence of agents has reshaped what machine learning engineers spend their time on. The post below goes a few levels deeper and zooms in on the machine learning modeling process itself, an area where recent developments in AI-assisted research have opened up exciting new frontiers that we are now actively exploring. Based on the combined insights of a small horde of MLEs, we will share some of the big wins, the disappointments, and the surprises we encountered along the way. Let's jump in. Big Picture Machine learning models permeate Instacart's marketplace, powering everything from search results to replacement recommendations and expected delivery times. Each of these models is carefully built, maintained, and iterated upon by our crafty MLEs. And while the hours spent on modeling tend to be extremely impactful for the company, the process itself is quite time-consuming and requires an MLE to make a myriad of both small and large decisions. These decisions include, among other things, the right modeling architecture, the appropriate choice for a large number of hyperparameters, the feature set to include, or the most suitable loss functions. All of these decisions are often grounded in a fairly lengthy review of the associated literature as well as a good dose of MLE intuition. Unfortunately, given the combinatorial complexity of this problem and the constraints on human time, MLEs can typically only explore a small part of the entire universe of modeling options, often leaving substantial value on the table. For a few months now, MLEs across Instacart have been exploring the development of methods and tools to tackle this constraint through AI-agent-assisted modeling loops. What follows ## Planetary prediction engine: Automating global models via Earth AI DevFeed: [Planetary prediction engine: Automating global models via Earth AI]() Original publisher: [Read original article]() Published: 2026-08-27T17:37:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Earth AI](), [Artificial Intelligence](), [data-engineering](), [Google](), [Machine learning](), [AI Development](), [Feature Engineering](), [Model Development]() Tags: [agents](), [ai](), [analytics](), [data-engineering](), [earth-ai](), [evaluation](), [feature-engineering](), [generative-ai](), [google](), [insights](), [machine-intelligence](), [mapping](), [research](), [training](), [validation](), [workflow](), [workflows]() ### AI overview Google Research introduces the Planetary Prediction Engine, an experimental Earth AI capability that autonomously performs geospatial data discovery, cleanup, feature engineering, model training, evaluation, and report generation from natural-language queries. The system targets applications including public health, food security, environmental risk, and socioeconomic analysis, reducing the stated workflow from weeks of manual data engineering to minutes. ### Source excerpt Earth AI ## Mistral x HUMAIN DevFeed: [Mistral x HUMAIN]() Original publisher: [Read original article]() Published: 2026-08-24T16:02:41Z Content type: article Language: en Sources: [Mistral AI Blog]() Topics: [AI Strategy](), [AI Infrastructure](), [AI Models](), [Model Development](), [model-deployment](), [Localization (l10n)](), [datacenter](), [Cybersecurity]() Tags: [ai](), [ai-infrastructure](), [ai-platform](), [arabic](), [data](), [data-center](), [deployment](), [enterprise](), [europe](), [financial-services](), [manufacturing](), [model-development](), [public-sector](), [security]() ### AI overview Mistral and HUMAIN announce a strategic collaboration to advance sovereign AI in Saudi Arabia and across the Middle East. The initiative covers AI infrastructure, advanced model development, localized Arabic-capable models, and deployment of AI solutions for regulated industries. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## An AI tool for prioritizing candidate biomarkers from wearable sensor data DevFeed: [An AI tool for prioritizing candidate biomarkers from wearable sensor data]() Original publisher: [Read original article]() Published: 2026-08-21T17:02:24Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Artificial Intelligence](), [Generative AI](), [data](), [Time Series](), [datasets](), [Temporal data](), [Training AI Models](), [Model Development]() Tags: [agents](), [ai](), [analysis](), [data](), [datasets](), [generative-ai](), [health-bioscience](), [memory](), [reasoning](), [research](), [series](), [time-series](), [training](), [validation](), [workflow]() ### AI overview The Biomarker Discovery Framework is a supervised multi-agent system for prioritizing biomarker candidates from wearable-sensor data. It combines hypothesis generation, statistical analysis, model training, adversarial validation, and literature-grounded reasoning in a traceable six-phase workflow. Across three cohorts, it recovered known clinical signals, found convergent biomarkers across independent datasets, and improved downstream prediction when demographic features were included. ### Source excerpt Generative AI ## 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 ## GPT-Red: Unlocking Self-Improvement for Robustness DevFeed: [GPT-Red: Unlocking Self-Improvement for Robustness]() Original publisher: [Read original article]() Published: 2026-07-15T10:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Artificial Intelligence](), [ai safety](), [OpenAI](), [Vulnerabilities](), [Responsibility & Safety](), [Training AI Models](), [Model Development](), [Deployment](), [Monitoring](), [Bug Bounty](), [browsers](), [data]() Tags: [ai](), [ai-safety](), [attacks](), [browsers](), [code](), [data](), [deployment](), [gpt](), [model](), [models](), [monitoring](), [openai](), [prompt-injection](), [safety](), [tool](), [tools](), [train](), [training](), [vulnerabilities]() ### AI overview OpenAI describes GPT-Red, an automated red-teaming model that uses adversarial attacks and self-play to find vulnerabilities, generate training data, and improve the robustness of future AI systems against prompt injection. The approach complements human and third-party red-teaming, layered safeguards, and real-time monitoring. ### Source excerpt Explore GPT-Red, OpenAI's automated red teaming system that uses self-play to improve AI safety, alignment, and prompt injection robustness. ## Training Orchestrator: Unifying Model Training at Yelp DevFeed: [Training Orchestrator: Unifying Model Training at Yelp]() Original publisher: [Read original article]() Author: Ying Wang and Nathan Sponberg, Software Engineer Published: 2026-07-14T00:00:00Z Content type: article Language: en Sources: [Yelp]() Topics: [Machine Learning & Artificial Intelligence](), [Orchestration](), [Apache Spark](), [Training AI Models](), [Model Development](), [configuration](), [configuration-management](), [Monitoring]() Tags: [configuration](), [machine-learning](), [maintenance](), [mlflow](), [model-training](), [orchestration](), [reproducibility](), [spark](), [tooling]() ### AI overview Yelp's Core Machine Learning Team developed Training Orchestrator to standardize how machine learning teams define and run Spark-based model training. The configuration-driven system addresses duplicated code, inconsistent configurations, limited local testing, scattered validation and monitoring, and poor reproducibility across environments. ### Source excerpt At Yelp, we train many machine learning models on different schedules. Applied machine learning teams all have their own set of Spark-based training batches, scripts, and configurations. Over time, these diverged, leading to duplicated code, subtle inconsistencies, and a growing maintenance burden. Yelp's Core Machine Learning Team has developed excellent tooling across our ML ecosystem over the years: feature stores for reproducible data, a unified training library for neural networks and gradient-boosted trees, seamless Spark integration, and MLflow services for model tracking and deployment. But there was still one key piece missing right in the middle: a standardized way to... ## Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T DevFeed: [Develop Humanoid Robot Policies End-to-End with NVIDIA Isaac GR00T]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-07-07T17:05:42Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [Robotics](), [Model Development](), [Open Source](), [Training AI Models](), [datasets](), [multimodal](), [Deployment](), [Simulation and Design](), [Tooling]() Tags: [ai-foundation-models](), [apache](), [building](), [data](), [deployment](), [developers](), [development](), [evaluation](), [featured](), [humanoid-robots](), [images](), [integrations](), [isaac](), [isaac-sim](), [manufacturing](), [model](), [multimodal](), [nvidia](), [open-source](), [robotics](), [robotics-simulation](), [robots](), [simulation-modeling-design](), [software](), [tooling](), [training](), [workflows]() ### AI overview NVIDIA Isaac GR00T Development Platform unifies humanoid robot development workflows, combining data collection, simulation-based training, evaluation, and deployment. The article highlights the open Isaac GR00T 1.7 vision-language-action model, which accepts language and images and can be adapted to robots, tasks, and environments through post-training. ### Source excerpt As more teams move from humanoid robot bring-up to task-specific skill development, the need for repeatable development workflows is growing. Building humanoids... ## Predicting model behavior before release by simulating deployment DevFeed: [Predicting model behavior before release by simulating deployment]() Original publisher: [Read original article]() Published: 2026-06-16T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [Deployment](), [Artificial Intelligence](), [Model Development](), [OpenAI]() Tags: [agent](), [agentic](), [ai](), [data](), [deployment](), [development](), [evaluation](), [model](), [model-development](), [models](), [openai](), [production](), [release](), [research](), [review](), [safety](), [synthetic](), [tool]() ### AI overview OpenAI describes Deployment Simulation, a privacy-preserving method that replays previous conversations with a candidate model to estimate undesired behavior before release. The approach complements evaluations and red-teaming, surfaces novel misalignment risks, supports agentic settings with tool use, and informs mitigations and deployment decisions. ### Source excerpt OpenAI introduces Deployment Simulation, a method to predict AI model behavior before deployment using real conversation data to improve safety and evaluation accuracy. ## Helping ChatGPT better recognize context in sensitive conversations DevFeed: [Helping ChatGPT better recognize context in sensitive conversations]() Original publisher: [Read original article]() Published: 2026-05-14T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [ChatGPT](), [Monitoring](), [Model Development](), [systems]() Tags: [awareness](), [chatgpt](), [model](), [monitoring](), [responses](), [safety](), [support](), [training](), [updates]() ### AI overview OpenAI describes safety updates that help ChatGPT recognize evolving signs of risk across sensitive conversations. The updates use conversational context to support more careful responses, including de-escalation, refusal of harmful details, and guidance toward support. ### Source excerpt Learn how new ChatGPT safety updates improve context awareness in sensitive conversations, helping detect risk over time and respond more safely. ## Kubeflow SDK User Survey 2026 - Feedback, Insights and Roadmap DevFeed: [Kubeflow SDK User Survey 2026 - Feedback, Insights and Roadmap]() Original publisher: [Read original article]() Author: Kubeflow SDK Team Published: 2026-04-28T05:00:00Z Content type: article Language: en Sources: [Kubeflow]() Topics: [SDK](), [Developer experience](), [Orchestration](), [Model Development](), [PyTorch](), [Kubernetes](), [GPU]() Tags: [2026](), [community](), [developer](), [developer-experience](), [gpu](), [insights](), [jupyter-notebook](), [kubernetes](), [orchestration](), [pytorch](), [sdk](), [survey](), [tooling]() ### AI overview The article reports findings from a Kubeflow SDK user survey involving practitioners from the Kubeflow ecosystem. It describes how Kubeflow is used in machine-learning workflows, highlights common tooling and components, and summarizes challenges involving infrastructure complexity, resource management, and debugging. ### Source excerpt To better understand the needs of our community, the Kubeflow SDK working group recently conducted a user survey focused on the SDK and developer workflows. The goal was to gather feedback from practitioners across the ecosystem about their current tooling, common challenges, and the features they would most like to see improved. ## Build a Domain-Specific Embedding Model in Under a Day DevFeed: [Build a Domain-Specific Embedding Model in Under a Day]() Original publisher: [Read original article]() Author: Steve Han; Rucha Apte; Sean Sodha; Oliver Holworthy Published: 2026-03-20T19:38:16Z Content type: tutorial Language: en Sources: [Hugging Face - Blog]() Topics: [Fine-tuning](), [synthetic-data](), [Synthetic Data Generation](), [Model Development](), [Inference](), [model-deployment](), [NeMo](), [Nemotron](), [NVIDIA NIM](), [TensorRT](), [Claude](), [API keys]() Tags: [api](), [fine-tuning](), [gpu](), [inference](), [nemo](), [nemotron](), [nim](), [nvidia](), [nvidia-nim](), [synthetic](), [synthetic-data](), [synthetic-data-generation]() ### AI overview A tutorial showing how to fine-tune a general-purpose embedding model for a specific domain in less than a day using synthetic question-answer pairs generated from domain documents. It covers data generation, contrastive training, retrieval evaluation, and deployment, using NVIDIA NeMo components and a Llama-Nemotron embedding model. ### Source excerpt With a single GPU and less than a day of training time, you can transform a general-purpose embedding model into one that truly understands your domain, no manual labeling required. To help you hit the ground running, we are also releasing a ready-to-use synthetic training dataset generated from NVIDIA's public documentation using this exact pipeline. ## Mistral AI partners with NVIDIA to accelerate open frontier models DevFeed: [Mistral AI partners with NVIDIA to accelerate open frontier models]() Original publisher: [Read original article]() Published: 2026-03-16T20:00:00Z Content type: news Language: en Sources: [Mistral AI Blog]() Topics: [Artificial Intelligence](), [Nvidia](), [foundation-models](), [Open Source](), [Nemotron](), [Fine-tuning](), [Model Development](), [NVIDIA DGX](), [DGX Cloud](), [Synthetic Data Generation](), [model architecture](), [NeMo]() Tags: [ai](), [dgx-cloud](), [fine-tuning](), [foundation-models](), [models](), [multimodal](), [nemo](), [nemotron](), [nvidia](), [open](), [optimization](), [post-training](), [synthetic](), [synthetic-data-generation]() ### AI overview Mistral AI announces a partnership with NVIDIA and its founding membership in the NVIDIA Nemotron Coalition. The collaboration will develop open frontier AI models using Mistral AI's model expertise and NVIDIA's compute, development tools, and synthetic-data pipelines. The coalition's first initiative will support the NVIDIA Nemotron 4 family, while Mistral AI also releases Mistral Small 4 for developers, researchers, and organizations. ### Source excerpt The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. ## White Paper on Data Science Technical Program Management DevFeed: [White Paper on Data Science Technical Program Management]() Original publisher: [Read original article]() Author: Sonu Jain Published: 2026-02-27T12:41:46Z Content type: article Language: en Sources: [Walmart Global Tech]() Topics: [Data Science](), [Model Development](), [Development](), [Software Engineering]() Tags: [collaboration](), [coverage](), [data-science](), [experimental](), [leadership](), [machine-learning](), [management](), [paper](), [retail](), [software-engineering](), [technical](), [technical-program-manager](), [training](), [validation](), [white-paper]() ### AI overview This white paper presents a structured approach to managing Data Science programs through technical program management. It discusses business alignment, cross-functional collaboration, data validation, model training and retraining, governance, and phased execution, using an inventory forecasting initiative as a real-world example. ### Source excerpt 1. Abstract Managing Data Science programs requires a structured approach to handle the complexities of data, model development, and business alignment. This whitepaper provides a comprehensive guide on the effective program management of Data Science programs by technical program managers. It highlights the critical role of Technical Program Managers (TPMs) in driving successful execution and outlines the key phases, challenges, and recommended best practices at every stage for effectively managing Data Science programs This white paper is grounded in a real-world inventory forecasting initiative aimed at improving stock availability and reducing overstock across multiple retail categories. The program involved cross-functional collaboration between Data Science, Engineering, Product, and Business teams to build predictive models that could dynamically adjust inventory levels based on demand signals. 2. Introduction Data Science has become a critical pillar of decision-making across industries, but organizations continue to struggle with operationalizing these initiatives. Unlike software development, which follows predictable sprint cycles, Data Science programs are inherently experimental -- requiring repeated cycles of data validation, model training, and retraining before they reach acceptable performance levels. This uncertainty often leads to misaligned expectations, delays in delivery, and inconsistent business impact. The iterative nature of model development makes predictability especially challenging: teams may require multiple iterations to achieve coverage and accuracy thresholds that satisfy business needs. Without structured program management, these efforts risk becoming siloed experiments rather than scalable, value-generating solutions. This whitepaper aims to address this gap by providing a practical framework for Technical Program Managers (TPMs) to manage Data Science programs effectively. It draws on real-world experience from a large-scale inve ## H Company's new Holo2 model takes the lead in UI Localization DevFeed: [H Company's new Holo2 model takes the lead in UI Localization]() Original publisher: [Read original article]() Author: Ramzi De Coster; Hamza Benchekroun; Aurélien Lac; Tony Wu; Pierre-Louis Cedoz; Kai Yuan; Mart Bakler; Antoine Bonnet; Aleix Cambray; Ronan Riochet Published: 2026-02-03T17:40:14Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Localization (l10n)](), [Model Development](), [Benchmark](), [skypilot](), [Kubernetes]() Tags: [agentic](), [benchmark](), [company](), [development](), [hugging-face](), [infrastructure](), [jobs](), [kubernetes](), [model](), [model-development](), [models](), [performance](), [release](), [research](), [scale](), [skypilot](), [training](), [ui]() ### AI overview H Company introduces Holo2-235B-A22B Preview, a research model for localizing UI elements in high-resolution interfaces. Its agentic mode iteratively improves predictions and achieves state-of-the-art results on ScreenSpot-Pro, while SkyPilot supports large-scale training across cloud providers and Kubernetes clusters. ### Source excerpt Two months since releasing our first batch of Holo2 models, H Company is back with our largest UI localization model yet: Holo2-235B-A22B Preview. This model achieves a new State-of-the-Art (SOTA) record of 78.5% on Screenspot-Pro and 79.0% on OSWorld G. Available on Hugging Face, Holo2-235B-A22B Preview is a research release focused on UI element localization. Agentic Localization High-resolution 4K interfaces are challenging for localization models. ## Normalized Entropy or Apply Rate? Evaluation Metrics for Online Modeling Experiments DevFeed: [Normalized Entropy or Apply Rate? Evaluation Metrics for Online Modeling Experiments]() Original publisher: [Read original article]() Author: Megan Chen Published: 2025-11-11T06:16:53Z Content type: opinion Language: en Sources: [Indeed]() Topics: [recommendation systems](), [Model Development](), [decision-making](), [benchmarking]() Tags: [a-b-testing](), [data-science](), [decision-making](), [evaluation](), [experiments](), [machine-learning](), [metric](), [models](), [ranking](), [unsorted]() ### AI overview Indeed examines whether model performance metrics or product metrics should guide online modeling experiments. It discusses how optimizing individual ranking models may not align with broader business goals and considers evaluation metrics for model rollouts. ### Source excerpt Introduction At Indeed, our mission is to help people get jobs. We connect job seekers with their next career opportunities and assist employers in finding the ideal candidates. This makes matching a fundamental problem in the products we develop. The Ranking Models team is responsible for building Machine Learning models that drive matching between job [...] ## Granite 4.0 Nano: Just how small can you go? DevFeed: [Granite 4.0 Nano: Just how small can you go?]() Original publisher: [Read original article]() Author: Kate Soule; Rameswar Panda Published: 2025-10-28T14:59:38Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [ibm](), [Large Language Model](), [Model Development](), [Open Source Models & Datasets](), [llama.cpp](), [MLX](), [vllm](), [Transformer](), [Loop Engineering](), [gemma](), [qwen](), [Google]() Tags: [apache](), [architecture](), [benchmarks](), [code](), [data](), [development](), [gemma](), [google](), [ibm](), [language-models](), [llama](), [llama-cpp](), [math](), [mlx](), [model](), [model-development](), [models](), [on-device](), [qwen](), [training]() ### AI overview IBM introduces Granite 4.0 Nano, a family of compact language models for edge and on-device applications. The release includes hybrid-SSM and traditional transformer variants ranging from roughly 350M to 1.5B parameters, supports vLLM, llama.cpp, and MLX, and is released under the Apache 2.0 license. The article reports strong performance across knowledge, math, code, safety, instruction-following, and tool-calling benchmarks. ### Source excerpt Today we are excited to share Granite 4.0 Nano, our smallest models yet, released as part of IBM's Granite 4.0 model family. Designed for the edge and on-device applications, these models demonstrate excellent performance for their size and represent IBM's continued commitment to develop powerful, useful, models that don't require hundreds of billions of parameters to get the job done. ## Neural Super Sampling is here! DevFeed: [Neural Super Sampling is here!]() Original publisher: [Read original article]() Author: EricSondhi; Will Lord Published: 2025-08-12T14:52:08Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Arm](), [GPU](), [Mobile](), [real-time](), [Unreal Engine](), [data](), [Model Development]() Tags: [ai](), [arm](), [gaming](), [gpu](), [latency](), [mobile](), [performance](), [plugin](), [real-time](), [train](), [unreal-engine]() ### AI overview Arm has released Neural Super Sampling (NSS), an AI-powered temporal upscaling model for real-time graphics on future mobile devices. It reconstructs high-resolution frames from lower-resolution temporal inputs, reducing GPU workload and compute cost for use cases such as mobile gaming and XR. The article also points developers to NSS resources, datasets, Unreal Engine plugins, and Vulkan-related learning paths. ### Source excerpt Neural Super Sampling (NSS), a next-generation AI-powered upscaling solution from Arm is released for graphics and gaming developers to start experimenting today! NSS is designed for real-time performance on future mobile devices with Arm Neural Technology. However, latency depends on implementation factors such as GPU configuration, resolution, and use case. In our Enchanted Castle demo video below, NSS reduced GPU workload by 50 percent. ## Logging and registering models with MLflow DevFeed: [Logging and registering models with MLflow]() Original publisher: [Read original article]() Author: Maria Vechtomova Published: 2025-07-31T18:49:09Z Content type: tutorial Language: en Sources: [MarvelousMLOps]() Topics: [databricks](), [MLOps](), [Model Development](), [scikit-learn]() Tags: [databricks](), [machine-learning](), [mlops](), [model](), [model-training](), [pipeline]() ### AI overview Lecture 4 in an MLOps with Databricks course explains how to log and register machine learning models with MLflow. It covers the standardized MLflow Model format, supported model flavors, custom PythonModel implementations, and a scikit-learn pipeline example. ### Source excerpt Lecture 4 of MLOps with Databricks course ## Ettin Suite: SoTA Paired Encoders and Decoders DevFeed: [Ettin Suite: SoTA Paired Encoders and Decoders]() Original publisher: [Read original article]() Author: Orion Weller; K Ricci; Marc Marone; Antoine Chaffin; Dawn Lawrie; Ben Van Durme Published: 2025-07-16T00:00:00Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [Artificial Intelligence](), [Machine Learning & Artificial Intelligence](), [Open Source](), [Large Language Model](), [Model Development](), [datasets]() Tags: [artificial-intelligence](), [bert](), [community](), [comparisons](), [datasets](), [embedding](), [llama](), [llm](), [model-development](), [nlp](), [open-source](), [open-source-collab](), [qwen](), [research](), [retrieval](), [training]() ### AI overview The article introduces Ettin, a suite of paired encoder-only and decoder-only language models ranging from 17M to 1B parameters. The models are trained with identical data, architectures, and recipes, enabling controlled comparisons between masked and causal language modeling. Ettin reports state-of-the-art performance for open-data models and explores converting models between encoder and decoder architectures. ### Source excerpt We're on a journey to advance and democratize artificial intelligence through open source and open science. [Next page]()