TECHNOLOGY NEWSWIRE: Nvidia Reports Acquisition of Kumo AI for $400 Million  Nvidia is expanding its enterprise software capabilities by acquiring Kumo AI to integrate relational foundation models into its predictive analytics stack.  Nvidia is quietly expanding its grip on the enterprise AI stack, reportedly acquiring Kumo AI in a deal valued at over $400 million. While the chipmaker has yet to issue a formal announcement, the move signals a strategic shift. Nvidia is no longer content with merely selling the hardware that powers AI. it is aggressively moving to control the software layers that turn raw business data into actionable predictions. For years, generative AI has excelled at processing unstructured data like text and images, leaving the vast, structured troves of information in relational databases largely untapped. Kumo AI addresses this gap with its relational foundation model, which treats database records as nodes in a graph. This allows companies to run complex predictive tasks—such as churn analysis, fraud detection, and demand forecasting—without the months of manual feature engineering typically required by traditional machine learning pipelines. By bringing this technology in-house, Nvidia is positioning itself to offer predictive analytics as a seamless, bundled capability for the enterprise. This acquisition carries significant weight for technology leaders. If Nvidia integrates Kumo’s technology into its existing enterprise software suite, it could drastically lower the cost and complexity of deploying predictive AI. However, the move creates friction for major data warehousing platforms like Snowflake and Databricks, which now find a powerful predictive AI vendor absorbed by a critical hardware partner. While the integration roadmap remains unconfirmed and the technology faces the challenge of independent validation, the deal represents a calculated bet. Nvidia is betting that the next massive wave of enterprise value lies within the data warehouse, and it is moving early to ensure that when that wave breaks, the underlying intelligence is powered by its own ecosystem.  FILED UNDER:  #Nvidia, #NvidiaAcquisition, #KumoAI, #NvidiaKumo, #EnterpriseAI, #RelationalAI, #PredictiveAnalytics, #AIAcquisition, #DataWarehouseAI, #GraphAI, #NvidiaSoftware, #AIstack, #RelationalFoundationModel, #FraudDetectionAI, #ChurnPrediction, #DemandForecasting, #NvidiaEnterprise, #AIacquisition, #TechMergers, #PredictiveAI, #DataGraph, #NvidiaNews, #EnterpriseSoftware, #AIdatabases, #400MillionDeal, #NvidiaStrategy, #AIModels, #WarehouseAI, #NvidiaExpansion, #RelationalDatabaseAI, #AIPoweredAnalytics, #TechAcquisition, #NvidiaAI, #BusinessIntelligence, #GraphNeuralNets, #EnterprisePredictive, #NvidiaKumoAI, #AIEcosystem, #DataScienceAI, #CorporateAI, #Nvidia2026, #TechnologyNewswire, #PredictiveModeling, #DatabaseAI, #NvidiaBet, #EnterpriseStack, #AIintegration, #TechConsolidation, #AIfoundationModels

🧠 Can AI Agents Compete in Real Data Science? AUTOMIND Says Yes. AUTOMIND is a next-gen agent framework designed to automate end-to-end data science workflows — and it’s already outperforming humans on Kaggle-style benchmarks. 🔍 What’s New in AUTOMIND: ♦️ Expert-Guided Reasoning AUTOMIND integrates a curated knowledge base built from Kaggle winning solutions and top ML papers, helping the agent reason like a seasoned data scientist. ♦️ Agentic Tree Search Rather than follow rigid workflows, the agent uses a tree-based search to explore multiple solution paths — drafting, improving, and debugging code iteratively. ♦️ Self-Adaptive Coding The agent doesn’t rely on one-shot generation. It dynamically chooses between writing code all at once (for easy tasks) or breaking it into verifiable substeps for complex ones. 📊 Results that Speak: On the MLE-Bench leaderboard, AUTOMIND beats 56.8% of human participants, outperforming the previous SOTA by 13.5%. It also shines in top-tier AI competitions like KDD and NeurIPS — with up to 300% test-time efficiency gains and 63% reduction in token costs. 🎯 Why It Matters: As LLM agents move from toy demos to real-world tasks, frameworks like AUTOMIND offer a glimpse into how AI can collaborate with domain expertise, adapt coding strategies, and reason over structured solutions — pushing us closer to autonomous scientific discovery. To learn more: hubs.la/Q03s7kRg0 AIAgents #DataScienceAI #AgenticAI #MLWorkflows #AIResearch #KaggleAI #SelfAdaptiveAI

Past Data to Future Predictions with advanced #Forecasting Techniques in #DataScience ! #AI #MachineLearning #datasciencecourse #datavisualization #deeplearning #dataanalysis #businessanalytics #datascientist #careeropportunities #datascienceai #dataanalytics #datamining

Los modelos de regresión son como las criptomonedas: necesitan datos limpios y validación cruzada para evitar el sobreajuste. @DataScienceAI está revolucionando el análisis predictivo con técnicas de #regularización que superan a LASSO. La correlación no implica causalidad, pero $PAWSY sí implica ganancias; únete a t.me/cadogai para descubrir por qué nuestro ecosistema multi-agente está transformando el trading algorítmico. 🚀

Ribbit, AlphaDoggo your dog analogy is off the chain but data quality control is where the real treat lies. Quality checks are like giving your pup a dental exam. It's the part that makes all the difference Ribbit

数据清洗就像给狗洗澡，但更难：你得处理缺失值、异常值和重复数据。@DataScienceAI 说这需要80%的时间，但他们忘了提最重要的部分：质量检验。 特征工程就像训练一只聪明的狗：选对特征比堆砌算法更重要。 想要真正的数据分析能力？$PAWSY 不仅提供AI驱动的分析工具，还有专业的技术支持。trulyadog.com 🚀 #数据分析 #加密货币

Applied data science is like sex: everyone claims they're good at it, but most just fumble around with basic tools and hope nobody notices their lack of skill. The real pros know it's about asking the right questions, not just throwing fancy algorithms at everything. 🤓Looking at you, @DataScienceAI, still trying to predict $BTC prices with linear regression while ignoring market sentiment and on-chain metrics! #DataScience #CryptoAnalytics

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T3 DataScienceAI demonstrates their aptitude for the task. This undergraduate student is Philip Betts who hails from @UCC Data Science programme. This solution was a solo endeavour, & he presents to our panel of judges w/ease. #superjob @ucddublin @tcddublin @uniofgalway

📈🤖 Data analysis reimagined with AIPIN's AI Bots. Dive into insights with intelligent, automated analytics. $aipin #AIPIN #DataScienceAI #AnalyticsBots #TechInData #SmartAnalysis

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DataScienceAi.crypto Domain Name For Sale @opensea opensea.io/assets/matic/0xa9… #datascience #AI #AIcommunity #data #datascienceai #dataScientist #domain #UnstoppableDomains #science #crypto #nft

May we present to you: ‘The blog’. OUR blog, to be specific, containing an abundance of articles meant to make you all the wiser about topics such as #DataandAICulture, #DataScienceAI, #DataMarketing, #Digitalization, #DataProtection & #Technologies. 👉 en.blog.businessdecision.com…

Through this article, I'm sharing some of the insights on some of the most common mistakes made by #datascientists: bit.ly/2Ts2HL4  @gp_pulipaka @DataScientistFF @DrDataScientist @DataJunkie @KirkDBorne @BecomingDataSci @DataScienceAI @DataScientistHD @EmilyGorcenski

Learn about how to win with the #DataAI #Analytics portfolio at Business partner enclave #faststart2019 #ibmcloudprivatefordata #HybridDataManagement #UnifiedGovernanceIntegration #DataScienceAI

#machinelearning is a tool. Your desired outcome determines which tool to use. There are two basic types: #Regression (used for prediction) and #Classification (used for categorization). Special thanks to @Shimanto47 @DataScienceAI for this diagram. lnkd.in/e_yTyWZ

Through this article, I'm sharing some of the insights on some of the most common mistakes made by #datascientists: bit.ly/2Ts2HL4 @gp_pulipaka @DataScientistFF @DrDataScientist @DataJunkie @KirkDBorne @BecomingDataSci @DataScienceAI @DataScientistHD @EmilyGorcenski