# Training AI Models The machine learning process of adjusting an AI model's parameters from examples. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK DevFeed: [University of Manchester Uses NVIDIA Earth-2 to Forecast Air Pollution Across the UK]() Original publisher: [Read original article]() Author: Isha Salian Published: 2026-09-16T05:00:42Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Artificial Intelligence](), [AI Models](), [Supercomputing](), [DGX Spark](), [Simulation](), [Training AI Models](), [data](), [Inference](), [real-time]() Tags: [ai](), [ai-for-good](), [ai-models](), [climate](), [compute](), [dgx-spark](), [government](), [healthcare](), [inference](), [nvidia](), [open-source](), [real-time](), [science](), [supercomputing](), [training](), [uk]() ### AI overview The University of Manchester is working with NVIDIA to use Earth-2 generative AI models to forecast air pollution across the U.K. The team trained Earth-2 CorrDiff on chemistry-climate simulation data using Isambard-AI, added StormCast for time-dependent forecasts using air-quality observations, and demonstrated workflows on DGX Spark. ### Source excerpt Air pollution is a serious public health risk, contributing to an estimated 30,000 deaths in the U.K. alone last year. Data-driven insights can help -- but computing air quality with traditional chemistry-based models is expensive, which limits how detailed they can be and how regularly they can be run. David Topping, a professor in the [...] ## Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions DevFeed: [Bolt.new tests Forge, offering more coding-model usage in exchange for anonymized developer sessions]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-15T18:47:23Z Content type: article Language: en Sources: [The New Stack]() Topics: [AI-assisted coding](), [Training AI Models](), [Open Source Models & Datasets](), [Agent Harness]() Tags: [agent](), [agent-harness](), [ai](), [ai-development](), [ai-engineering](), [ai-models](), [coding-agents](), [data](), [developer-tools](), [developers](), [training]() ### AI overview Bolt.new is testing Forge, a research preview for individual Pro subscribers that offers up to 50 times more usage of open-weight coding models in exchange for opting in to share anonymized coding sessions. The sessions may include prompts, source code, fix traces, and conversations with the coding agent, and will support an Arcee AI project to train a trillion-parameter-class open-weight model. ### Source excerpt Bolt.new, StackBlitz's browser-based AI development platform, is testing a new trade with developers: more coding-model usage in exchange for training The post Bolt is giving developers 50x more compute. But there's a catch. appeared first on The New Stack. ## Interpreting Pangram DevFeed: [Interpreting Pangram]() Original publisher: [Read original article]() Author: Armin Ronacher Published: 2026-09-14T00:00:00Z Content type: opinion Language: en Sources: [Armin Ronacher]() Topics: [Artificial Intelligence](), [Large Language Model](), [datasets](), [Training AI Models]() Tags: [ai](), [llm](), [model](), [openai](), [training](), [training-data]() ### AI overview The article discusses Pangram, an AI-text detector that classifies passages as human-written, AI-generated, or mixed. It explains that Pangram manufactures training data from human-authored text and LLM-generated rewrites and edits, then describes an experiment using Opus 5 to generate text intended to read as entirely AI-generated. ### Source excerpt Yesterday David Sacks wrote a tweet and within a few minutes people did, what they usually do, and they asked Pangram if it was AI. And Pangram said it's entirely AI generated. To which David replied that these AI detectors are bogus. Now Pangram has a pretty low false positive rate, but if you have ever used an LLM as a writing assitant, you will have probably noticed that it claims your posts 100% AI, even though you don't feel like they are. Pangram itself is a trained model, that attempts to detect segments of text as being definitely human, definitely AI and a mixture of the two. If you want to know how it works, they published a paper. The short summary is that they are manufacturing its own training data by starting from collections of known human authored text. An LLM is then tasked to understand the text and write a fresh new text on the same topic. They also let the LLM perform partial edits on that original human text and through that they can pick up on these co-authored details. Pangram claims their model to have rates of 0.0041% false AI accusations and 0.34% missed AI text. So now that we know this I figured it might be fun to have an LLM re-create David's tweet. I first came up with a prompt. And when I say I came up with that prompt I in fact used an LLM to propose to me from that tweet what I might want to say for the structure. I'm sure if you ask Pangram about if the above text is AI, it will probably say so, but that's not really the point. The point is that I then used Opus 5 to generate a text which reads entirely AI generated. If you are curious, this is the prompt I used: Original prompt to generate a David Sacks like tweet Please write an 8 paragraph tweet about "Pacing the Frontier", not longer than 350 words. Context of this tweet is this post by Dario: https://darioamodei.com/post/we-must-pace-the-frontier And this tweet by Sam Altman: "I agree with Dario that we need to pace the frontier. This has been a primary topic of discussions we' ## Building a reliable cloud native foundation for distributed AI training DevFeed: [Building a reliable cloud native foundation for distributed AI training]() Original publisher: [Read original article]() Author: Abhi Kulkarni and Shishir Jindal, Atlassian Published: 2026-09-11T11:00:00Z Content type: article Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Training AI Models](), [Machine learning](), [distributed-systems](), [GPU](), [cloud-infrastructure](), [Network design](), [Inference]() Tags: [ai-training](), [blog](), [distributed-training](), [gpu](), [infrastructure](), [storage]() ### AI overview The article explains how to make multi-node AI training reliable by treating inter-node communication, shared storage, hardware placement, network topology, and validation as platform concerns. It identifies RDMA for GPU-node communication and Lustre for concurrent training-data and checkpoint access. ### Source excerpt AI workloads are changing what platform teams need from infrastructure. Provisioning GPUs and standing up a cluster no longer makes a platform "AI-ready." Once training spans more than one node, the bottlenecks show up in places... ## 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. ## Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies DevFeed: [Physical AI Takes the Wheel: How the World's Robotaxi Leaders Are Building With NVIDIA Technologies]() Original publisher: [Read original article]() Author: Ali Kani Published: 2026-09-10T16:00:04Z Content type: article Language: en Sources: [NVIDIA Blog]() Topics: [Training AI Models](), [SDKs]() Tags: [ai-models](), [ai-training](), [artificial-intelligence](), [autonomous-vehicles](), [cosmos](), [customer-stories](), [datasets](), [driving](), [mobility](), [nvidia](), [nvidia-blackwell](), [nvidia-dgx](), [nvidia-drive](), [nvidia-halos](), [omniverse](), [physical-ai](), [simulation-and-design]() ### AI overview NVIDIA describes an open robotaxi platform for training AI driving models, simulation and safety validation, and real-time in-vehicle computing. ### Source excerpt The global robotaxi market -- physical AI's first commercial breakthrough -- is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world's busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is [...] ## Why don't machine learning research agents overfit? DevFeed: [Why don't machine learning research agents overfit?]() Original publisher: [Read original article]() Author: Martin Bertran Lopez; Aaron Roth Published: 2026-09-10T15:03:39Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [machine learning overfitting](), [Training AI Models](), [AI research agents](), [Occam's razor machine learning]() Tags: [agentic-ai](), [agents](), [ai-research-agents](), [benchmark](), [benchmark-overfitting-machine-learning](), [compressibility-and-memorization](), [compression-and-generalization](), [generalization-in-machine-learning](), [generative-ai](), [information-bottleneck-overfitting](), [language-models](), [llm-compression-theory](), [machine-learning](), [machine-learning-overfitting](), [machine-learning-research](), [occam-s-razor-machine-learning](), [research](), [validation](), [why-don-t-ml-models-overfit-on-benchmarks]() ### AI overview The article explains why repeated evaluation on held-out benchmarks can cause overfitting, then frames the apparent contradiction in machine learning research, where benchmark-driven iteration is widespread. It also summarizes research suggesting that compressible models limit memorization. ### Source excerpt New research indicates that AI agents learn compressible models of data, which don't have enough space to enable memorization. ## K2 Horizon just shipped as six new fully open models -- developers aren't fully convinced DevFeed: [K2 Horizon just shipped as six new fully open models -- developers aren't fully convinced]() Original publisher: [Read original article]() Author: Adrian Bridgwater Published: 2026-09-09T12:00:00Z Content type: news Language: en Sources: [The New Stack]() Topics: [Open Source Models & Datasets](), [foundation-models](), [Training AI Models]() Tags: [ai](), [ai-models](), [developer-tools](), [foundation-models](), [open](), [open-source](), [release](), [training]() ### AI overview The Institute of Foundation Models introduced K2 Horizon, a six-model open-source foundation-model fleet. The article examines its promised release of training artifacts and notes that some data, code, checkpoints, and a final 32B model were not yet available at launch. ### Source excerpt Based in the Emirati capital, Abu Dhabi, the Institute of Foundation Models (IFM) introduced K2 Horizon last week. This group The post K2 Horizon just shipped as six new fully open models -- developers aren't fully convinced appeared first on The New Stack. ## Java's age is its AI superpower DevFeed: [Java's age is its AI superpower]() Original publisher: [Read original article]() Author: Ryan Donovan Published: 2026-09-09T04:45:00Z Content type: opinion Language: en Sources: [Stack Overflow Blog]() Topics: [coding](), [Training AI Models]() Tags: [agent](), [agentic](), [ai](), [buiilding-software](), [coding](), [data](), [ecosystem](), [history](), [ibm](), [java](), [podcast](), [se-stackoverflow](), [se-tech](), [sponsored](), [training]() ### AI overview A podcast episode argues that Java's longevity, training-data footprint, libraries, and agentic tooling make it a strong fit for coding agents. ### Source excerpt Ryan welcomes Markus Eisele to the program to talk about why your coding agent should be writing Java. ## Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI DevFeed: [Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI]() Original publisher: [Read original article]() Author: Dmytro Hryshchenko, Abhijeet Dhumal Published: 2026-09-09T03:32:28Z Content type: tutorial Language: en Sources: [Red Hat](), [Red Hat Developer]() Topics: [Fine-tuning](), [voice ai](), [Training AI Models](), [Artificial Intelligence](), [distributed-training](), [GPU](), [lora](), [data](), [Open Source]() Tags: [ai](), [artificial-intelligence](), [distributed-training](), [fine-tuning](), [gpu](), [lora](), [open-source](), [speech](), [text-to-speech](), [training](), [voice](), [workflow]() ### AI overview This tutorial explains how to fine-tune the open source Orpheus-3B text-to-speech model for Turkish using Red Hat OpenShift AI and Kubeflow Trainer. It describes packaging distributed training in a TrainJob, scaling across nodes and GPUs, and using LoRA to keep memory usage below 16 GB. The reported result reduces speech errors by more than 90% compared with the base model. ### Source excerpt Can't Read, Won't Buy. That is the title CSA Research gave its survey of 8,709 consumers across 29 countries, and the numbers justify it: 76% prefer to buy in their own language, and 40% will never buy in another. The same rule governs what your product says out loud. The post Personalize your product's text-to-speech voice for any language: Fine-tuning with Kubeflow Trainer on Red Hat OpenShift AI appeared first on Red Hat Developer. ## Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod DevFeed: [Pathway's brain-inspired architecture development on Amazon SageMaker HyperPod]() Original publisher: [Read original article]() Author: Paulo Aragão Published: 2026-09-08T19:12:51Z Content type: article Language: en Sources: [Artificial Intelligence]() Topics: [Language models](), [Training AI Models](), [compression and generalization](), [LLM evaluation / benchmarking]() Tags: [amazon](), [amazon-sagemaker](), [amazon-sagemaker-hyperpod](), [announcements](), [architecture](), [benchmark](), [chain-of-thought](), [distributed-training](), [intermediate-200](), [pytorch](), [transformer-architecture]() ### AI overview Pathway describes BDH, a brain-inspired architecture that performs reasoning in latent space rather than producing chain-of-thought token traces. The article covers its recurrent internal memory, its contrast with transformer limitations, and scaling training with Amazon SageMaker HyperPod. ### Source excerpt Pathway's Baby Dragon Hatchling (BDH) is a brain-inspired, post-transformer architecture that reasons in latent space instead of emitting chain-of-thought tokens. See how Pathway develops and scales BDH on Amazon SageMaker HyperPod, and how BDH-CQ set a new cost-efficiency mark on the ARC-AGI-1 benchmark. ## Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes DevFeed: [Amazon SageMaker Feature Store introduces UpdateRecord for feature-level writes]() Original publisher: [Read original article]() Author: Mona Mona Published: 2026-09-08T18:29:15Z Content type: release Language: en Sources: [Artificial Intelligence]() Topics: [AI, ML & Data Engineering](), [Training AI Models](), [AWS IAM]() Tags: [amazon-dynamodb](), [amazon-elasticache](), [amazon-machine-learning](), [amazon-sagemaker](), [amazon-sagemaker-ai](), [announcements](), [api](), [artificial-intelligence](), [aws-identity-and-access-management-iam](), [intermediate-200](), [technical-how-to]() ### AI overview Amazon SageMaker Feature Store adds the UpdateRecord API for atomic feature-level updates without reading or rewriting an entire record. ### Source excerpt Amazon SageMaker Feature Store now supports feature-level writes. With the new UpdateRecord API, you can update one or more feature values in a single call without reading or rewriting the entire record. It is available for both the Standard (Amazon DynamoDB) and In-Memory (Amazon ElastiCache) online store tiers. ## China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes DevFeed: [China Merchants Bank Wins CNCF End User Case Study Contest for Unifying AI Training and Inference on Kubernetes]() Original publisher: [Read original article]() Author: Haley White Published: 2026-09-08T01:54:31Z Content type: news Language: en Sources: [Cloud Native Computing Foundation]() Topics: [Kubernetes](), [AI Platform](), [AI Inference](), [Training AI Models](), [kueue](), [Fine-tuning](), [Cloud Native Ecosystem](), [datacenter]() Tags: [ai](), [ai-infrastructure](), [ai-training](), [announcements](), [case-study](), [china](), [compute](), [cost](), [fine-tuning](), [inference](), [kubernetes](), [kueue](), [lora]() ### AI overview China Merchants Bank won a CNCF case-study contest for a Kubernetes-based AI platform that shares nearly 10,000 accelerator cards across training, fine-tuning, and online inference. The bank reports increased average accelerator utilization and lower inference costs. ### Source excerpt New cloud native platform lifted average accelerator compute utilization from 35% to more than 60% and cut inference cost per 1 million tokens by more than 60% Key Highlights SHANGHAI, China - KubeCon + CloudNativeCon +... ## Safety overview: GPT-6 Astra DevFeed: [Safety overview: GPT-6 Astra]() Original publisher: [Read original article]() Published: 2026-09-03T00:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [Vulnerabilities](), [Training AI Models](), [ai observability](), [Cryptography]() Tags: [cybersecurity](), [encryption](), [evaluation](), [gpt](), [jailbreak](), [model](), [monitoring](), [safety](), [training]() ### AI overview GPT-6 Astra is presented as a broadly deployed model with Critical cybersecurity capability. The article outlines protections against harmful cyber actions, stronger jailbreak resistance, alignment improvements, evaluations, and monitoring. ### Source excerpt GPT-6 Astra is our most capable broadly deployed model and our first to reach the Critical level of cybersecurity capability under our Preparedness Framework. ## VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University DevFeed: [VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T10:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [High-Performance Computing](), [Data Infrastructure](), [InfiniBand](), [Post-quantum cryptography](), [Deployment](), [Training AI Models](), [Hardware](), [Low Latency](), [Quantum Computing]() Tags: [ai](), [data-infrastructure](), [deployment](), [enterprise](), [enterprise-storage](), [hardware](), [high-performance-computing](), [infiniband](), [low-latency](), [post-quantum-cryptography-pqc](), [quantum](), [storage]() ### AI overview VDURA has moved its storage platform at New Mexico State University into full production to support AI and high-performance computing research. The deployment combines NVMe flash and high-density HDD tiers through a global namespace over InfiniBand, allowing separate scaling of performance and capacity. It also supports NMSU's post-quantum cryptography research and large-scale data pipeline projects. ### Source excerpt VDURA has completed the deployment of its data platform at New Mexico State University (NMSU), moving the system into full production. The infrastructure is designed to serve the university's research community with a high-durability, high-throughput storage environment tailored specifically for artificial intelligence and high-performance computing (HPC) workloads. NMSU, which holds Carnegie R1 status and manages The post VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University appeared first on StorageReview.com. ## Build Your Own Face Recognition Tool With Python DevFeed: [Build Your Own Face Recognition Tool With Python]() Original publisher: [Read original article]() Author: Kyle Stratis Published: 2026-09-01T14:00:00Z Content type: tutorial Language: en Sources: [Real Python]() Topics: [Machine learning](), [object-detection](), [Training AI Models](), [Command-line interface]() Tags: [command-line](), [computer-vision](), [machine-learning](), [python](), [testing](), [train](), [tutorial]() ### AI overview A Python tutorial for building a command-line face-recognition tool that detects faces in images, trains and validates a model, and labels detected faces with bounding boxes. ### Source excerpt In this tutorial, you'll build your own face recognition command-line tool with Python. You'll learn how to use face detection to identify faces in an image and label them using face recognition. With this knowledge, you can create your own face recognition tool from start to finish! ## PyTorch vs. TensorFlow: Differences, Performance, and How to Choose DevFeed: [PyTorch vs. TensorFlow: Differences, Performance, and How to Choose]() Original publisher: [Read original article]() Author: NICOLAS PIRO, DATA SCIENTIST AND AI DEVELOPER @ TOPTAL Published: 2026-08-27T04:00:00Z Content type: comparison Language: en Sources: [Toptal Blog]() Topics: [Training AI Models]() Tags: [deep-learning](), [deployment](), [experimentation](), [infrastructure](), [performance](), [pytorch](), [tensorflow](), [tooling]() ### AI overview A comparison of PyTorch and TensorFlow for deep-learning experimentation, model design, production workflows, tooling, and infrastructure considerations. ### Source excerpt This comprehensive guide explores how PyTorch and TensorFlow shape deep-learning work in 2026, from experimentation and model design to production workflows, ecosystem tooling, and infrastructure considerations. ## IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining DevFeed: [IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining]() Original publisher: [Read original article]() Published: 2026-08-26T00:00:00Z Content type: article Language: en Sources: [Apple Machine Learning Research]() Topics: [Language models](), [compression and generalization](), [Training AI Models](), [Inference Performance]() Tags: [compression](), [generative](), [inference](), [language-models](), [large-language-models](), [model](), [training]() ### AI overview This paper presents IDEA Prune, an integrated enlarge-and-prune pipeline for generative language model pretraining. It combines enlarged-model training, iterative structured pruning, and recovery under one cosine annealing learning-rate schedule, with experiments compressing 2.8B models to 1.3B. ### Source excerpt Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown promise in token efficiency compared to training target-size models from scratch. In this paper, we advocate incorporating enlarged model pretraining, which is often ignored in previous works, into pruning. We study the enlarge-and-prune pipeline as an integrated system to address two critical questions: whether it is worth pretraining an enlarged model even when the model is never deployed, and how to optimize the... ## How Adobe Reduced GPU Idle Time in Generative AI Training Through Faster Data Access and Checkpointing DevFeed: [How Adobe Reduced GPU Idle Time in Generative AI Training Through Faster Data Access and Checkpointing]() Original publisher: [Read original article]() Author: Maddie Presland Published: 2026-08-25T15:22:30Z Content type: article Language: en Sources: [Backblaze Blog | Cloud Storage & Cloud Backup]() Topics: [GPU](), [Training AI Models](), [cloud-infrastructure](), [networking](), [data]() Tags: [ai-ml](), [ai-training](), [b2cloud](), [cloud](), [cloud-storage](), [compute](), [ethernet](), [featured](), [featured-cloud-storage](), [gpu](), [model-training](), [networking]() ### AI overview Adobe's generative AI training pipeline left roughly two-thirds of GPU time waiting for data. The article attributes the waste to storage and retrieval bottlenecks, networking limits, and checkpointing overhead, and describes Adobe's use of a high-performance networking fabric and fragmented checkpoint storage to reduce delays. ### Source excerpt Adobe's experience reveals why GPUs sit idle during AI model training: slow storage, insufficient throughput, and uneven workloads. Learn how storage bottlenecks and uneven data loading leave expensive GPUs idle--and how always-hot, high-throughput object storage keeps AI training pipelines running efficiently at scale while reducing wasted compute costs. The post Why Your GPU Is Sitting Idle: The Data Pipeline Problem No One Talks About appeared first on Backblaze Blog | Cloud Storage & Cloud Backup ## 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 Generative Recommenders Are Redefining RecSys at Scale DevFeed: [How Generative Recommenders Are Redefining RecSys at Scale]() Original publisher: [Read original article]() Author: Elizabeth Goodman Published: 2026-08-20T16:00:00Z Content type: article Language: en Sources: [NVIDIA Developer](), [NVIDIA Technical Blog]() Topics: [recommendation systems](), [AI, ML & Data Engineering](), [Training AI Models](), [Language models]() Tags: [agentic-ai-generative-ai](), [ai-inference](), [data-center-cloud](), [developer-tools-techniques](), [embedding](), [featured](), [generative](), [gpu](), [inference](), [latency](), [llms](), [machine-learning](), [machine-learning-artificial-intelligence](), [recommenders-personalization](), [scale]() ### AI overview The article examines the shift toward generative recommender systems and the challenges of training and serving them at large scale. ### Source excerpt Recommender systems (RecSys) are one of the most ubiquitous machine learning problems in the consumer internet industry yet notoriously difficult to train and... ## Adobe Firefly: Simplified observability with Amazon Managed Prometheus DevFeed: [Adobe Firefly: Simplified observability with Amazon Managed Prometheus]() Original publisher: [Read original article]() Author: Dev Arora Published: 2026-08-13T00:14:19Z Content type: article Language: en Sources: [AWS Architecture Blog]() Topics: [observability](), [GPU](), [Amazon EKS](), [Amazon Web Services](), [Monitoring](), [telemetry](), [migration](), [Training AI Models]() Tags: [adobe](), [amazon](), [amazon-eks](), [amazon-elastic-kubernetes-service](), [amazon-managed-service-for-prometheus](), [amazon-web-services-aws](), [aws](), [compute](), [cpu](), [customer-solutions](), [data](), [deployment](), [efficiency](), [gpu](), [infrastructure](), [jobs](), [kubernetes](), [metrics](), [observability](), [performance](), [prometheus]() ### AI overview Adobe Firefly migrated critical GPU infrastructure metrics from self-managed Prometheus to Amazon Managed Service for Prometheus. The article describes the observability challenges of large-scale model training on Amazon EKS, including high-cardinality GPU, compute, memory, and network telemetry, and reports 28x faster GPU metric queries with improved reliability and operational efficiency. ### Source excerpt Learn how Adobe Firefly achieved 28x faster GPU metric queries by migrating from self-managed Prometheus to Amazon Managed Service for Prometheus, with improvements in query performance, infrastructure reliability, and operational efficiency. ## Training 100x Cheaper Retrieval models Neon and Castform DevFeed: [Training 100x Cheaper Retrieval models Neon and Castform]() Original publisher: [Read original article]() Author: Pranav Aurora Published: 2026-08-05T12:00:00Z Content type: article Language: en Sources: [Blog -- Neon Docs]() Topics: [Retrieval Augmented Generation (RAG)](), [Training AI Models](), [Machine learning](), [Open Source Models & Datasets](), [Large Language Model](), [Open Source](), [Latency](), [Database]() Tags: [agentic](), [agents](), [cost](), [embedding](), [infra](), [llms](), [machine-learning](), [model](), [models](), [open-source](), [performance](), [post-training](), [product](), [rag](), [retrieval](), [rl](), [scale](), [search](), [systems](), [tools](), [training]() ### AI overview The article explains how Castform uses reinforcement-learning post-training to improve open-weight models for agentic retrieval. It contrasts multi-step retrieval with one-shot embedding search, emphasizing the cost and latency of repeated frontier-model calls and the potential for smaller open models to perform specific search tasks more cheaply. ### Source excerpt "Most teams' best training data is just sitting in their databases. The problem is that turning raw data into something usable is hard, and letting agents read, search, and mutate data cheaply at scale requires advanced infra. Pointing Castform at Neon skips both." -- Ying Hang Seah, cofounder, Castform ## How controllers from industrial machinery can coordinate multitask machine learning DevFeed: [How controllers from industrial machinery can coordinate multitask machine learning]() Original publisher: [Read original article]() Author: Theodore Vasiloudis Published: 2026-07-30T17:26:47Z Content type: article Language: en Sources: [Amazon Science homepage]() Topics: [Machine Learning & Artificial Intelligence](), [Graphs](), [Training AI Models](), [Neural Network](), [data](), [Algorithm](), [systems]() Tags: [data](), [machine-learning](), [model](), [multitask-learning](), [research](), [self-supervised-learning](), [train](), [training]() ### AI overview ControlG addresses conflicting objectives in graph self-supervised learning by allocating computational capacity to one objective at a time and using a proportional-integral-derivative controller to select which objective receives attention next. ### Source excerpt Instead of compromising among parameter updates dictated by different training objectives, ControlG allocates computational capacity to objectives sequentially and dynamically. 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