TL;DR: Use a three‑pronged strategy—metadata‑driven retry budgets, selective tool‑schema filtering, and quadratic‑aware context compression—to cut tok

Concurrency sweeps help you right-size a generative AI endpoint on Amazon SageMaker AI by systematically benchmarking it at increasing load levels. This post walks through deploying a model, running automated concurrency sweeps with the CreateAIBenchmarkJob API, and using the results to make data-driven capacity decisions about fleet size.

Using Bridge360 Metatheory Model norms, Google Gemini Notebook explores how one may go about governing the tacit ontologies of Jev and Palantir, and how, for good measure, everyone may need to deploy ...

Amazon Connect Talent is an AI hiring solution built for talent acquisition leaders managing scaled hiring. It delivers AI-led interviews, data-driven assessments, and consistent evaluation, helping recruiters identify strong candidates more efficiently while providing applicants with a flexible interview experience. Informed by decades of Amazon’s hiring science, Amazon Connect Talent provides transparency for every assessment, interview, and candidate score, enabling recruiters to stay in control of final hiring decisions.

Learn how to build a fully serverless pipeline that automatically collects Git metrics from GitHub and GitLab and visualizes them in interactive Amazon Quick Sight dashboards, giving engineering teams near-real-time delivery analytics at low cost.

Groups Conversations All groups and messages Sign in     New Outlook’s Copilot-Driven Summaries 0 views Eyal Estrin unread, 7:09 AM (1 hour ago)    to https://office365itpros.com/2026/09/15/new-outlook-copilot/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Integrated artificial intelligence (AI) ensembles identify highly potent repurposed drugs against Streptococcus pneumoniae. Prospective virtual screening of 6747 drugs uncovers nine active candidates...

TL;DR: LILA’s closed‑form KS‑based neuron scoring outperforms PruneNet’s RL‑driven pruning without any calibration data, making it the most pragmatic

Amazon Quick custom permissions let you enforce least-privilege access by toggling features per user. This post walks through four patterns to automate custom permissions across the user lifecycle: a RegisterUser API parameter, account and role defaults, event-driven Amazon EventBridge and AWS Lambda automation, and a retroactive batch update script.

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Reprints and permissions...

Water quality forecasting has long been divided between process-based mechanistic models, which are resource-intensive and difficult to calibrate, and data-driven approaches that often fail to capture the nonlinear, nonstationary dependencies inherent in environmental time series. While deep learning has shown promise in fields like energy and traffic forecasting, its application to water quality has largely treated models as black boxes, with little understanding of how specific architectural components interact with ecological dynamics. This gap has made informed model selection difficult and has hindered the development of forecasting frameworks that can adapt to different bloom scenarios and forecast horizons. Based on these challenges, a systematic investigation into how core AI modules align with ecological time scales is urgently needed. Researchers from Xiamen University, Wenzhou University, the University of Hong Kong, the Chinese Academy of Sciences, and the Helmholtz Centre for Environmental Research—UFZ report (DOI: 10.1016/j.ese.2026.100755) their findings on August 30, 2026, in Environmental Science and Ecotechnology. The team compared two conventional baselines and seven advanced deep forecasting architectures—including Crossformer, DLinear, Informer, NSTransformer, PatchTST, SegRNN, and TimesNet—using high-frequency monitoring data from Germany’s Königshütte Reservoir and China’s Yazidang Reservoir. Their goal was to move beyond model-level comparisons and uncover which architectural modules drive...

HyperPod InstantStart is an open source control plane that composes Amazon EKS orchestration with the managed capabilities of Amazon SageMaker HyperPod. It drives the same guarded operations through both a web interface and an AI agent, turning cluster bootstrap, capacity, training, inference, and storage into dependable, agent-driven infrastructure.

Groups Conversations All groups and messages Sign in Machine Learning, Deep Learning and AI     AI-driven development lifecycle using Amazon Bedrock AgentCore 0 views Eyal Estrin unread, 9:12 PM (16 minutes ago)    to https://aws.amazon.com/blogs/machine-learning/ai-driven-development-lifecycle-using-amazon-bedrock-agentcore/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Groups Conversations All groups and messages Sign in     Reduce MTTR with AI-driven RCA using AWS DevOps Agent and Splunk 0 views Eyal Estrin unread, 8:32 PM (24 minutes ago)    to https://aws.amazon.com/blogs/mt/reduce-mttr-with-ai-driven-rca-using-aws-devops-agent-and-splunk/ Eyal Estrin Author | Cloud Architect | AWS • Azure • GCP Insights Social: @eyalestrin Connect: https://linktr.ee/eyalestrin Blog: https://security-24-7.com Reply all Reply to author Forward

Engineering teams adopting the AI-Driven Development Lifecycle (AI-DLC) often struggle to turn concepts into working code. This post walks through two reference implementations on Amazon Bedrock AgentCore, Kiro, and Claude Code: an SQL-to-ER-diagram generator and a multi-agent code security analyzer that put the AI-DLC construction phase into practice.

An agent that works in a notebook is not an agent in production. This post walks through migrating a LangGraph customer support agent to Amazon Bedrock AgentCore in two stages: onto Runtime, Gateway, and Memory, then to model-driven planning on Strands Agents, retiring operational burdens along the way.

Hart, J. K. & Martinez, K. Environmental sensor networks: a revolution in the earth system science? Earth Sci. Rev. 78, 177–191 (2006). Kurth, T. et al. Exascale deep learning for climate analytics (IEEE, 2018). Reddy, D. S. & Prasad, P. R. C. Prediction of vegetation dynamics using NDVI time series data and LSTM. Model. Earth Syst. Environ. 4, 409–419 (2018). Reichstein, M. et al. Deep learning and process understanding for data-driven earth system science. Nature 566, 195–204 (2019). Alber, M. et al. Integrating machine learning and multiscale modeling — perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit. Med. 2, 1–11 (2019). Iten, R., Metger, T., Wilming, H., Del Rio, L. & Renner, R. Discovering physical concepts with neural networks. Phys. Rev. Lett. 124, 010508 (2020). Raissi, M., Perdikaris, P. & Karniadakis, G. E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686–707 (2019). Schmidt, M. & Lipson, H. Distilling free-form natural laws from experimental data. Science 324, 81–85 (2009). Brunton, S. L., Proctor, J. L. & Kutz, J. N. Discovering governing equations from data by sparse identification of nonlinear dynamical systems...

1/ We have a new research target been confirmed from previous analyses on #MECFS using #machinelearning and #networkanalysis methods named PGC-1α / PPARGC1A (see below - arrow). It was identified in 2...