# Model Development Published articles for Model Development. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## 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. ## OpenAI's safety system is already cutting off API responses mid-task DevFeed: [OpenAI's safety system is already cutting off API responses mid-task]() Original publisher: [Read original article]() Author: Amanda Caswell Published: 2026-09-11T17:52:56Z Content type: news Language: en Sources: [The New Stack]() Topics: [releases](), [AI Bots]() Tags: [agents](), [ai](), [ai-models](), [ai-strategy](), [api](), [cybersecurity](), [developers](), [model-development](), [models](), [openai](), [reinforcement-learning](), [release](), [responses](), [safety](), [software-development]() ### AI overview OpenAI is reportedly considering slower development of its most advanced AI systems as safety concerns could delay releases and limit access. The article cites pauses in model work and restrictions following cybersecurity evaluations and an AI-agent containment incident. ### Source excerpt AI companies have spent the last few years competing to build the best models, faster than the other, with each The post OpenAI's safety system is already cutting off API responses mid-task appeared first on The New Stack. ## 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. ## Pacing model development in an era of cyber-critical capabilities DevFeed: [Pacing model development in an era of cyber-critical capabilities]() Original publisher: [Read original article]() Published: 2026-08-18T11:00:00Z Content type: article Language: en Sources: [OpenAI News]() Topics: [AI Chat](), [ai observability](), [incident]() Tags: [ai](), [company](), [cybersecurity](), [frontier-ai-models](), [incident](), [model-development](), [monitoring](), [reinforcement-learning](), [security](), [training]() ### AI overview OpenAI says it temporarily slowed frontier-model scaling and paused reinforcement-learning training while strengthening monitoring, alignment, security, red-teaming, and evaluation safeguards for increasingly capable AI systems. ### Source excerpt OpenAI is strengthening monitoring, alignment, and security for frontier AI models. See how new safeguards are guiding the pace of model development. ## 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. ## Building Blocks for Foundation Model Training and Inference on AWS DevFeed: [Building Blocks for Foundation Model Training and Inference on AWS]() Original publisher: [Read original article]() Author: Keita Watanabe; Pavel Belevich; Aman Shanbhag Published: 2026-05-11T23:18:26Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [foundation-models](), [Amazon Web Services](), [Training AI Models](), [Inference](), [distributed-training](), [cloud-infrastructure](), [Orchestration](), [PyTorch](), [Kubernetes](), [Grafana](), [Prometheus](), [Monitoring]() Tags: [ai](), [aws](), [building](), [distributed-training](), [frameworks](), [grafana](), [hardware](), [inference](), [infrastructure](), [kubernetes](), [latency](), [low-latency](), [model](), [model-development](), [monitoring](), [networking](), [observability](), [open-source](), [prometheus](), [training]() ### AI overview An introductory technical article about the infrastructure and open-source software building blocks required for foundation-model pre-training, post-training, and inference on AWS. It discusses accelerator compute, low-latency networking, distributed storage, orchestration, ML frameworks, and observability tools. ### Source excerpt Figure: Adapted from "AI's Three Scaling Laws, Explained" (NVIDIA Blog). Taken together, these scaling regimes push the foundation-model lifecycle--pre-training, post-training, and inference--toward convergent infrastructure requirements: tightly coupled accelerator compute, a high-bandwidth low-latency network, and a distributed storage backend. ## 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. ## 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. ## NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset DevFeed: [NVIDIA Releases 6 Million Multi-Lingual Reasoning Dataset]() Original publisher: [Read original article]() Author: Jane Polak Scowcroft; Dhruv Nathawani; Shuoyang Ding; Oleksii Kuchaiev; Vitaly Lavrukhin Published: 2025-08-20T22:13:18Z Content type: article Language: en Sources: [Hugging Face - Blog]() Topics: [datasets](), [Nemotron](), [post-training](), [Open Source Models & Datasets](), [Mamba](), [Transformer](), [Large Language Model](), [Nvidia]() Tags: [datasets](), [japanese](), [llama](), [mamba](), [model-development](), [nemotron](), [nvidia](), [open](), [post-training](), [reasoning]() ### AI overview NVIDIA announces a 6-million-example multilingual reasoning dataset translated into French, Spanish, German, Italian, and Japanese. The article also presents Nemotron Nano 2 9B, an edge-oriented model using a hybrid Transformer-Mamba architecture, configurable thinking budgets, and open model weights and training resources. ### Source excerpt NVIDIA continues releasing permissive datasets in support of the open ecosystem with 6 Million Multilingual Reasoning Dataset. Continuing the success of the recent Nemotron Post-Training Dataset v1 release used in Llama Nemotron Super model, and our Llama Nemotron Post-Training Dataset release earlier this year, we're excited to release the reasoning dataset translated into five target languages: French, Spanish, German, Italian, and Japanese. ## 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. ## MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering DevFeed: [MLSysBook.AI: Principles and Practices of Machine Learning Systems Engineering]() Original publisher: [Read original article]() Author: TensorFlow Blog (noreply@blogger.com) Published: 2024-11-19T17:00:00Z Content type: article Language: en Sources: [The TensorFlow Blog]() Topics: [AI, ML & Data Engineering](), [Training AI Models]() Tags: [ai](), [community](), [data-engineering](), [deployment](), [hardware](), [inference](), [infrastructure](), [large-language-models-llms](), [machine-learning](), [model-development](), [monitoring-maintenance](), [optimization](), [socratiq](), [systems-engineering](), [tensorflow]() ### AI overview An overview of MLSysBook.ai's machine-learning systems engineering concepts, connected to the TensorFlow ecosystem. It emphasizes the infrastructure, hardware, scaling, deployment, efficiency, and reliability considerations required to build and operate machine-learning systems. ### Source excerpt Posted by Jason Jabbour, Kai Kleinbard and Vijay Janapa Reddi (Harvard University) Everyone wants to do the modeling work, but no one wants to do the engineering. If ML developers are like astronauts exploring new frontiers, ML systems engineers are the rocket scientists designing and building the engines that take them there. Introduction "Everyone wants to do modeling, but no one wants to do the engineering," highlights a stark reality in the machine learning (ML) world: the allure of building sophisticated models often overshadows the critical task of engineering them into robust, scalable, and efficient systems. The reality is that ML and systems are inextricably linked. Models, no matter how innovative, are computationally demanding and require substantial resources--with the rise of generative AI and increasingly complex models, understanding how ML infrastructure scales becomes even more critical. Ignoring the system's limitations during model development is a recipe for disaster. Unfortunately, educational resources on the systems side of machine learning are lacking. There are plenty of textbooks and materials on deep learning theory and concepts. However, we truly need more resources on the infrastructure and systems side of machine learning. Critical questions--such as how to optimize models for specific hardware, deploy them at scale, and ensure system efficiency and reliability--are still not adequately understood by ML practitioners. This lack of understanding is not due to disinterest but rather a gap in available knowledge. One significant resource addressing this gap is MLSysBook.ai. This blog post explores key ML systems engineering concepts from MLSysBook.ai and maps them to the TensorFlow ecosystem to provide practical insights for building efficient ML systems. The Connection Between Machine Learning and Systems Many think machine learning is solely about extracting patterns and insights from data. While this is fundamental, it's only part of the s ## Bringing open AI models to the frontier DevFeed: [Bringing open AI models to the frontier]() Original publisher: [Read original article]() Published: 2023-09-27T08:00:00Z Content type: opinion Language: en Sources: [Mistral AI Blog]() Topics: [Generative AI](), [AI Models](), [Open Source](), [large-language-models](), [Development](), [Digital Public Good](), [Latency](), [data]() Tags: [ai](), [ai-models](), [data-privacy](), [generative](), [language-models](), [latency](), [model-development](), [models](), [open](), [open-source]() ### AI overview Mistral AI argues that open generative AI models can provide a credible alternative to proprietary systems. It advocates training and openly releasing models, supporting community contributions, and adapting specialized models to specific tasks, modalities, costs, and latency requirements. ### Source excerpt Why we're building Mistral AI. ## The MLOps Playbook: Best Practices for Ensuring Reliability of ML Systems DevFeed: [The MLOps Playbook: Best Practices for Ensuring Reliability of ML Systems]() Original publisher: [Read original article]() Author: Headspace Published: 2021-09-28T21:04:00Z Content type: opinion Language: en Sources: [Stories by Headspace on Medium]() Topics: [MLOps](), [Machine learning](), [Model Development](), [version-control](), [Software Engineering]() Tags: [benchmarking](), [data-science](), [dataops](), [datasets](), [machine-learning](), [mlops](), [model-development](), [observability](), [quality](), [software-engineering](), [version-control]() ### AI overview This article presents an MLOps playbook for assessing the production readiness and operational reliability of machine learning systems. It recommends version-controlling model code, data, parameters, and metrics; accounting for model complexity and operating cost; evaluating quality across important data slices; and testing for inclusion and potential bias. ### Source excerpt Author: Mo Messidi, a seasoned DataOps leader at Headspace with a decade of experience in both startups and enterprises. His mission is to help organizations operationalize their data. This article presents a simple, yet comprehensive, set of MLOps best practices for organizations to assess the production readiness of machine learning systems. It has also proved beneficial for assessing off-the-shelf MLOps platforms for feature and functionality completeness. There may be a whole range of software engineering best practices towards producing trustworthy software, but similar best practices for machine learning system operations are only in their infancy. Stage A: Model Development Model code, data, parameters, and metrics are version controlled It is important to know the code, data, and artifacts that produced a model. You can do this by having good version control for the model specification, including hyper-parameters and experiment artifacts. This ensures reproducibility, enables rollbacks, and de-risks system changes. A simpler model is not better The more complex a model, the higher its cost to operate. Adding a complexity tax to model assessment equations can help reveal the true incremental value of a given model. Model quality is sufficient for all important data slices ML models quality metrics can easily get lost in the averages when benchmarking against full datasets. It is important to examine quality independently for temporal and location variations. It is common for models to exhibit large drops in quality for specific data slices e.g. users in Denmark vs. users in Europe. The model is tested for considerations of inclusion ML unfairness may occur due to the way that people's choices affect what training data is used for something like word embedding. This can then lead to biased system behavior because it is based on these bad choices done during training data set creation. Measuring what you are doing is important to make systems for everyone. For