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Oct 24Sat
  1. AWS Machine Learning Blog34

    How Bayer Crop Science built an agentic decision engine on Amazon Bedrock AgentCore with AWS Unified Operations

    Bayer Crop Science built the Agentic Decision Engine (ADE), a multi-agent natural language interface over its Connected Decision Engine, running on Amazon Bedrock AgentCore with LangGraph orchestration and 22 specialist agents. In its first phase, ADE resolves about 80 percent of user questions without an engineer in the loop, and exposing APIs through AgentCore Gateway cut per-API integration overhead by more than 99 percent. The post details five design decisions, production readiness work such as task-level observability and cost attribution, and ongoing operations with AWS Unified Operations.

  2. AWS Machine Learning Blog20

    Visualize points and routes with GeoJSON in Amazon Quick Sight layer maps

    Amazon Quick Sight layer maps now support Point and LineString geometry types from GeoJSON files, letting users plot individual locations and routes joined to a Quick Sight dataset by a shared key. The post explains the required GeoJSON structure and dataset join, compares layer map points with the native point map, and covers advanced techniques for controlling the viewport, layering geometries, and updating GeoJSON files via Amazon S3 and the Quick Sight APIs.

  3. AWS Machine Learning Blog22

    Orchestrating SageMaker AI endpoint auto scaling with AWS CDK

    Updating a SageMaker AI real-time endpoint configuration fails through CloudFormation or CDK because SageMaker AI requires the scalable target to be deregistered first, and manual deregistration leaves CloudFormation unable to track Application Auto Scaling resources. The post presents a reusable CDK construct that replaces the native scalable target and scaling policy resources with three custom resources—Deregister, Register Scalable Target, and Attach Scaling Policy—ordered with explicit dependencies and driven by a ConfigHash so endpoint and auto scaling changes deploy together. It includes a walkthrough using the sample-sagemaker-endpoint-autoscaling GitHub repository and guidance on integrating the construct into an existing stack.

Oct 23Fri
  1. Hugging Face Blog38

    Does Your Model Run on AWS Trainium? Look It Up

    Hugging Face introduces a Space that checks whether popular Hub models run unmodified on AWS Trainium via the torch-neuronx backend, reporting sharding, wall time, and peak memory, and providing the run.py script used. Results come from the public aws-neuron/torch-neuronx-model-support dataset, with one Parquet file per neuronx-cc and library version pair. Tests currently cover text-generation and image-text-to-text Transformers models fitting on at most 4 Trainium2 devices (16 cores), generating 32 tokens.

  2. AWS Machine Learning Blog18

    How U.S. Hunger connects leaders and data using Amazon Quick

    U.S. Hunger, which reports delivering more than 250 million meals, uses Amazon Quick to let CEO Rick Whitted explore Full Cart datasets directly and to give 17 colleagues across leadership, technology, finance, and compliance shared Spaces for KPI definitions, financial planning, and research. Data from its Impact Manager platform is refreshed from SQL Server into SPICE nightly at 2 AM ET, and the organization plans a Q4 2026 enterprise KPI framework routing NetSuite, Salesforce, and other systems through Boomi into Amazon Redshift.

  3. AWS Machine Learning Blog34

    Train Qwen3 on AWS Trainium with torchtitan

    This post walks through training Qwen3 three ways on a single trn2.48xlarge using torchtitan-neuron: via Hugging Face Transformers v5 as a modeling backend, via torchtitan's native Qwen3 by changing config.model_spec, and via NKI mixture-of-experts kernels from the torchtitan-neuron recipes, all using the same torchrun launch pattern. It also shows how to load and save published safetensors checkpoints, attach a custom NKI rope_hf kernel through torchtitan's override registry, and scale with data and tensor parallelism across 64 logical NeuronCores.

  4. AWS Machine Learning Blog38

    Adaptive agentic intelligence framework using Strands Agents

    The Adaptive Agentic Intelligence Framework (AAIF), built on Strands Agents, uses a small local language model (llama3.2:1b) as an orchestrator that routes routine requests to local SLMs via Ollama and complex requests to cloud models through Amazon Bedrock. The post walks through an IoT monitoring reference implementation with tools for device telemetry, local vision analysis, and Bedrock-based expert insights, and describes cloud-only, hybrid, and air-gapped deployment configurations. It also outlines converting tools into MCP servers for distributed tool calling and applying Amazon Bedrock Guardrails to the cloud inference path.

  5. AWS Machine Learning Blog58

    5 production challenges in multi-agent systems and how Deep Insight solved them

    AWS's Solutions Architecture team describes Deep Insight, an open-source multi-agent system (Coordinator, Planner, Supervisor, Coder, Validator, Reporter, Tracker) that turns a CSV file and a natural-language prompt into a validated DOCX analysis report with charts. The post, part one of three, maps five production challenges to a stack: Strands Agents SDK Graph and Agents-as-Tools patterns for execution flow, role-specific Claude models on Amazon Bedrock, Amazon Bedrock AgentCore for managed deployment, AWS Fargate plus Application Load Balancer for isolated code execution, and DynamoDB, Amazon SNS and Amazon Cognito for monitoring. It also cites LG Electronics' ChatInsight and TechRecon as reuse of the same architecture.

  6. AWS Machine Learning Blog20

    Securing Quick access from corporate networks with AWS PrivateLink

    This walkthrough explains how to connect on-premises data sources to Amazon Quick privately using AWS PrivateLink VPC endpoints and Quick VPC connections, so traffic stays off the public internet over Direct Connect or Site-to-Site VPN. It covers endpoint creation for website and API access, DNS resolution via Route 53 private hosted zones, endpoint policies, execution roles, data source VPC endpoints, required external domains, and enforcing VPC endpoint restrictions. It also addresses identity federation with IAM Identity Center and notes that browser sign-in still requires outbound access to public AWS sign-in endpoints.

  7. NVIDIA Blog34

    NVIDIA Open Models Power a New Wave of European Public Sector Projects

    NVIDIA partners and European governments are adopting open models for public-sector AI, including the European Commission's deepset-based AI@EC platform, Poland's healthcare and employment agentic systems, and Germany's Materna Sinfonie and secunet sovereign cloud. New Nemotron-Personas datasets for the UK and Germany provide 1 million synthetic personas each for testing public-service models, while France's CANUT selected LINAGORA and Docaposte for a sovereign workspace and Greece's KIEFER builds an agentic factory on DGX SuperPOD. NVIDIA's Open Models Codefest named Pleias, Zehnlab.ai, Disseqt, Algoritmica GmbH and ABA Technology as its top five teams.

  9. NVIDIA Blog30

    Kazakhstan Expands Compute for Its AI Future

    During Jensen Huang's visit to Astana, Firebird signed an investment agreement with Kazakhstan securing a U.S. export license for 45,000 NVIDIA Grace Blackwell GPUs, with a first phase under construction with Kazakhtelecom at Data Center Valley targeting commercial launch in Q3 2027. Kazakhtelecom has already deployed 400 NVIDIA H200 and 480 NVIDIA B300 GPUs in Astana and Almaty, and NVIDIA announced an investment in Kazakhstan-founded startup Higgsfield AI. Huang and Deputy Prime Minister Zhaslan Madiyev discussed Kazakh-language models, physical AI for mining and agriculture, and AI security via NVIDIA OpenShell at the Alem.AI fireside chat.

  12. Hugging Face Blog62

    Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox

    Microsoft and Hugging Face describe using ThinkingBox's executable terminal-database-state checks as binary rewards to fine-tune Qwen3.8-27B with GRPO for 50 steps on an offline-selected pool of 187 tasks, raising pass@1 from 51.70% to 60.78% and tasks passing all 20 attempts from 38 to 107 on the 507-task ThinkingBox-Bench, while pass@20 dipped slightly from 453 to 446. The post details offline policy-relative difficulty selection via Jeffreys posteriors, the reward loop, cost-per-dependable-task falling from $24.36 to $8.00, and releases the training stack in microsoft/thinkingbox-training.

    Why it matters: It offers a concrete, reproducible recipe for converting stateful agent grading into RL rewards, plus an honest account of the repeatability-versus-coverage tradeoff.

  14. AWS Machine Learning Blog43

    Amazon Polly delivers a major upgrade to its Generative engine

    Amazon Polly's upgraded Generative engine is now generally available, offering more expressive and accurate speech with low latency as a drop-in upgrade that requires no code changes. It adds 14 new voices across 9 locales, including the first generally available Generative voices for Hindi and Finnish, bringing the catalog to 57 voices across 11 languages and 25 locales, with expansion to AWS GovCloud (US-West). Pricing is unchanged, and the engine continues to support SSML and the Bi-Di Streaming API.

  15. AWS Machine Learning Blog31

    How IskayPet unified corporate knowledge with Amazon Bedrock Managed Knowledge Bases

    IskayPet, an Iberian pet care company with over 330 stores and 4,500 employees, built a corporate knowledge assistant on Amazon Bedrock Managed Knowledge Bases that answers employee questions across retail operations, veterinary care, people and onboarding, and procurement in four languages and three countries without managing RAG infrastructure. The solution uses the native SharePoint connector with advanced indexing, a dedicated knowledge base per assistant scoped to specific sites or libraries, document-level access control tied to existing SharePoint permissions, and hybrid search with reranking. Built with Kiro and Claude Haiku 4.5, it reportedly cut time to find an operational answer from 3–5 minutes to about 7 seconds in roughly the first two months in production.

  16. AWS Machine Learning Blog58

    Announcing new ways to customize models with SageMaker AI, without managing GPUs

    AWS announced the general availability of training sessions in Amazon SageMaker AI serverless model customization, letting customers write and coordinate their own post-training loop, including custom loss functions and reward signals, while SageMaker AI handles GPU allocation, distributed execution, fault recovery, and checkpoint storage. The sessions support techniques such as SFT, DPO, RLVR, RLAIF, and multi-turn RL, with LoRA or full fine-tuning, and are available with support for gpt-oss-20b, gemma-4-31B-it, Qwen3.6-27B, and Qwen3.8-27B. Customers can start via the SageMaker Python SDK and cookbook or in SageMaker Studio, with data kept in their own S3 buckets and IAM, KMS, and VPC controls.

  17. AWS Machine Learning Blog58

    Write your own training loop — without managing GPUs — with Amazon SageMaker training session APIs

    Amazon SageMaker AI introduces training session APIs that let you write a custom training loop in Python on your local machine while SageMaker runs GPU-bound operations such as forward_backward(), optim_step(), and sample() on managed accelerators with your base model and LoRA adapter. The APIs are organized around ServiceClient, TrainingClient, and SamplingClient in the SageMaker Python SDK, and the post walks through a minimal GRPO loop on GSM8K (held-out accuracy rising from 56% to 75% in 10 steps) plus a Code RL recipe on competitive programming problems (held-out accuracy from 40% to 50.5%). A companion cookbook provides ready-to-run SFT and RL recipes.

  18. AWS Machine Learning Blog34

    Deploy AI in a HIPAA-eligible environment with Amazon Quick

    This AWS post explains how to deploy Amazon Quick, a HIPAA-eligible service, for healthcare workloads that touch protected health information, covering covered entities and business associates, the distinction between HIPAA-eligible and HIPAA-compliant, and the unstructured (knowledge base) and structured (Amazon Quick Sight) PHI data paths. It details the controls Amazon Quick provides—managed infrastructure, encryption, RBAC and SSO, CloudTrail and CloudWatch vended logs—alongside what it does not do, such as de-identification and lake-level governance handled by AWS Glue, Glue DataBrew, and Lake Formation. It closes with a fictional health plan walkthrough, irreversible day-zero decisions (customer managed key, identity model, ACL-aware knowledge bases), and a step-by-step implementation guide.

  19. AWS Machine Learning Blog38

    Stream low-latency speech from AI agents using Kokoro TTS on Amazon SageMaker AI

    This AWS post describes streaming each sentence an agent produces to a hosted Kokoro-82M text-to-speech model on a SageMaker AI real-time endpoint, so synthesis starts on the first sentence instead of waiting for the full response. It covers bidirectional WebSocket handling, an incremental sentence chunker, and two transports for returning audio to the browser, reporting that incremental sending cut mean time to first audio byte from 329.3 ms to 156.7 ms in isolation and made bidi mode 2.4 to 3.5 times faster than batch in a deployed agent.

  20. AWS Machine Learning Blog32

    Stop guessing auto scaling targets for TTS on Amazon SageMaker AI

    This AWS post deploys Kokoro-82M, an 82-million-parameter non-autoregressive TTS model, as a custom container on an Amazon SageMaker AI real-time endpoint via AWS CDK stacks, then replaces a guessed auto scaling target with values derived from two live measurements: Q_max (the highest per-instance concurrency that still meets a 3,000 ms p95 time-to-first-audio SLO, measured at 50) and T_total (end-to-end scale-out time, measured at 260.2 s and planned against 320.2 s). Scaling uses the native ConcurrentRequestsPerModel metric, and a five-command tool chain (drift, qmax, ttotal, plan, validate) computes the scale-out threshold (37.5), scale-in threshold (25.0) and asymmetric cooldowns by arithmetic. The authors note the methodology generalizes to other latency-sensitive, concurrency-bound real-time SageMaker AI endpoints, while the specific numbers hold only for Kokoro-82M on ml.g5.xlarge.

  21. AWS Machine Learning Blog38

    How Rescale built an AI triage agent with Strands Agents and AI-DLC

    Rescale used the open source AI-Driven Development Lifecycle (AI-DLC) framework on AWS to build an agent that classifies and routes nightly end-to-end test failures, going from problem definition to staging in five days and to production within weeks. The agent runs in Rescale's Argo Workflows CI/CD pipeline on Amazon EKS, uses Claude on Amazon Bedrock for classification, retrieves historical failure patterns from a knowledge base, and queries pod logs via MCP, producing structured remediation reports. In production, classification agreement with human triage rose from 44% to 68%, and time blocking production deployment while failures are analyzed fell 20%.

  22. Hugging Face Blog58

    OlmoEarth v1.3: State-of-the-art embeddings for the whole planet

    OlmoEarth v1.3 is a new satellite embedding model that reads a year of Sentinel-1, Sentinel-2 and Landsat time series to produce one 128-dimensional vector per 10-meter pixel, with the first 64 dimensions usable as a smaller embedding. Precomputed annual global embeddings are shared on Source Cooperative, and a browser-based OlmoEarth Embeddings Explorer lets users explore maps, find similar places, and train small classifiers without a GPU or sign-in. The 128-dimensional embeddings scored highest on 20 of 27 evaluations against AlphaEarth Foundations and Tessera v2, and the model took 53% of v1.2's computing time and 18% of v1's, with weights, pretraining data and code all open.

  23. Microsoft Research58

    Flint-interactive: A language for creating interactive data visualizations

    Microsoft Research introduces Flint-interactive, a language built on Flint that lets agents and developers create interactive data visualizations using high-level semantic events and update operators rather than low-level visualization code. Flint's compiler resolves raw triggers like clicks and drags into semantic events and translates update operators into rendered canvas changes, aiming to improve reliability and reduce generation cost. It supports presets such as linked-brush, a programming API for bespoke interactions, and connections between charts, tables, text, and application state.

Oct 22Thu
  1. NVIDIA Blog60

    NVIDIA Unveils Minerva to Bring End-to-End Observability for AI Factories

    NVIDIA launched Minerva, an AIOps platform that combines telemetry from infrastructure and AI workloads and applies purpose-built AI models to surface timely, actionable insight. It builds a dynamic knowledge graph mapping GPUs, SuperNICs, switches, cables and the jobs running on them, analyzes billions of data points per hour to detect anomalies and predict failures, and includes a native AI assistant plus connections to external agentic AI tools. IREN is among the partners using Minerva, and Aolani, Bitdeer AI, IBM, Jio, Lambda, Quake AI and Tesla plan to deploy it, with Dell Technologies, Lenovo and Supermicro offering it in their AI factory operations portfolios.

    Why it matters: It details a new NVIDIA observability platform and its named deployment partners, useful for readers tracking AI data center operations tooling.

  2. Google DeepMind58

    Beyond tree cover: mapping the world’s forest types

    Google DeepMind and Google Research, with the World Resources Institute and IIASA, released Forest Typology (ForTy) 2020, a global 10-meter-resolution map classifying land into six FAO-based categories: primary forest, naturally regenerating forest, planted forest, plantation forest, tree crops and agroforestry, and other land. An accompanying early time series tracks annual changes from 2017 to 2025, including where natural forests were replaced by plantations or farms. Validated against more than 9,000 expert-reviewed sites, ForTy identified the correct forest type 90% of the time and distinguished forest from non-forest land 95% of the time, and the map and time series are freely available on Google Earth Engine under an open license.

  3. Hugging Face Blog38

    Ensemble forecasting with IBM Granite time-series models

    IBM introduces Granite Timeseries Ensemble, a recipe that combines four pre-trained Granite models (PatchTST-FM-r1, PatchTST-FM-r2, FlowState-r1.1, and TTM-r3) through one extensible forecasting interface to return median and quantile forecasts without retraining or fitted ensemble weights. The default Probabilistic Ensemble pools member quantiles with equal influence, while an adaptive IQR Ensemble weights members by forecast spread; on 97 GIFT-Eval configurations both ensembles beat all four individual members on aggregate MASE and average MASE Rank, with the Probabilistic Ensemble reaching 0.683 MASE versus 0.685 for the best single member. The authors note the MASE margin is small, that PatchTST-FM-r2 retains the lowest aggregate CRPS/MWQL, and that the recipe forecasts channels independently and loads four checkpoints, increasing inference time and memory.

  4. OpenAI News22

    What FDE means at OpenAI

    This item states that it explains what forward deployed engineering (FDE) means at OpenAI, but provides no further details beyond that framing.

  5. NVIDIA Blog40

    Leading European Financial Services Organizations Embrace Open Models to Build Proprietary Intelligence

    At GTC Berlin, European banks, payment companies and data providers showcased production-scale AI built on NVIDIA accelerated computing and open weight models running on their own infrastructure, including Revolut's PRAGMA foundation model serving an initial 80 million customers and Starling Bank's agentic model trained on U.K. sovereign cloud. Open weight models let institutions like bunq, NatWest, Modulr and Santander build proprietary intelligence at controlled cost, while LSEG, RavenPack, HSBC and Stripe reported speedups such as 10x faster tick-data analysis and 42x faster data pipelines for fraud detection.

  6. NVIDIA Blog60

    NVIDIA Cosmos Opens New Tools to Train, Evaluate and Deploy World Models

    NVIDIA expanded its Cosmos platform with new training tools, simulation checkpoints and Cosmos Data Factory blueprints, including five cloud-native blueprints for tasks such as autonomous vehicle dataset generation and defect image generation. Cosmos Framework now extends training, optimization and deployment tools as agent skills to open models like GR00T, Alpamayo, QWEN and Pi0.5, while new Cosmos 3 Nano simulation variants and updated NIM microservices offer up to 2x faster inference. The post cites Porsche, ANYbotics, Ultralytics and ADNOC as users of the platform.

    Why it matters: The post details concrete tooling, model support and named customer deployments, useful for readers tracking physical AI and world-model infrastructure.

  7. NVIDIA Blog36

    How Companies Are Using NVIDIA DSX to Break the Power Bottleneck

    NVIDIA DSX AI Factory Platform lets companies plan, simulate and run the grid and AI factory as one system, addressing power bottlenecks such as over $300 billion in backlogged equipment and over 200 gigawatts of projects in U.S. interconnection queues. The platform combines DSX Sim, DSX Flex, DSX MaxLPS and DSX Exchange, with partners including GE Vernova, Hitachi Energy, Schneider Electric, Siemens Energy, Phaidra and Emerald AI. Phaidra reports about 10% more compute within the same power envelope, and Emerald AI's approach is modeled to unlock up to 100 gigawatts on the existing U.S. grid.

  8. NVIDIA Blog25

    NVIDIA Transforms European Retail, From Digital Discovery to Physical Operations

    European retailers are using NVIDIA-accelerated open models and physical AI across commerce and fulfillment, including Shopify post-training NVIDIA Nemotron 3.5 Lightning for its Sidekick GraphQL subagent, which it says surpasses a leading frontier model in accuracy on an internal benchmark while cutting serving costs by 90% and delivering responses 7x faster. Otto Group extended its autonomous warehouse initiative, built with Reply on NVIDIA Omniverse and running on Google Cloud, to its largest fulfillment center in Haldensleben, Germany, and Nebius is showcasing three early-access NVIDIA retail blueprints at GTC Berlin. Other cited deployments include Zooplus with Infosys, Photoroom with Vestiaire Collective, L'Oréal with Omi and NVIDIA ALCHEMI, Mirakl, ALLSIDES, and CATCHES with AMIRI.

  9. NVIDIA Blog40

    Pioneers Across Europe Deploy NVIDIA Earth-2 Open Models to Power Real-World Weather and Climate Prediction

    NVIDIA Earth-2 offers developers and researchers a family of open AI models and tools, running on NVIDIA computing, for global weather prediction, detailed regional forecasts and environmental research. StormScope EU, a regional short-term forecasting model trained on European weather and climate data, uses EUMETSAT satellite observations to predict cloud conditions across 16 Meteosat channels at 10-minute intervals, and is being shown in a live interactive demo at GTC Berlin built with Earthmover. Organizations including AXA, Vaisala Xweather, the University of Manchester, TotalEnergies and Space42 are adapting Earth-2 models such as FourCastNet 3, CorrDiff and PhysicsNeMo for cyclone risk, lightning-enhanced forecasting, U.K. air quality, solar forecasting and radar-based surface-height estimation.

  12. Hugging Face Blog75

    NVIDIA Kumo Relational Outperforms Supervised Machine Learning on Relational Data, No Training Required

    NVIDIA introduces Kumo Relational, a pre-trained relational foundation model that predicts directly on enterprise databases via in-context learning in a single forward pass, requiring no task-specific training, feature engineering, or table flattening. Across 41 tasks on 15 databases, it beats supervised relational deep learning models and expert-engineered gradient-boosted tree pipelines, and on the SAP SALT benchmark it surpasses AutoGluon ensembles by 8% in mean reciprocal rank (MRR). It scales to databases with 500B+ rows and is available through NVIDIA's open-source structured-data-models and kumo-engine libraries or as an NVIDIA NIM.

    Why it matters: Useful for ML teams weighing whether to replace production XGBoost pipelines with a pre-trained relational model, though the reported gains come from NVIDIA's own paper and benchmarks.

  13. Hugging Face Blog58

    How Specializing the Vocabulary of SLMs Can Cut Inference Cost by 77% in Real Applications

    Dharma-AI describes VocabX, a method that rebuilds a small model's tokenizer around the cost of each token rather than its frequency, combining sub-word merges with multi-word "macro" tokens under a shared cost criterion. In a production Portuguese essay grader built on Qwen3-4B, the approach cut average response length from 350 to 101 tokens and measured inference cost by 77.0% with the same base model and training recipe, while the bench score rose from 0.703 to 0.810. The article previews results from a VocabX paper still under review, noting the numbers may change before publication.