Glenn Harp

Snowflake Intelligence: From Data to Decisions - Atrium's AI: AI projects fail when data quality is poor and siloed. At its core, the Snowflake platform solves this by giving access to unified insights with ...

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Warehouses were built for humans running reports—not agents firing evolving queries at ms cadence. We break down stateful scaling limits, manual tuning traps, and how agent-native engines close the gap (with MCP + StarRocks). Dive into the details: https://hubs.la/Q03DL_7D0 #Lakehouse #DataInfrastructure #DataAnalytics #DataEngineering

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If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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Data engineers can use Agent Bricks AI functions to modernize their data engineering practice by automating data extraction, summarization, and consolidation.

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🚀 Ready to elevate your AI/ML strategy? CData's Jerod Johnson joins industry experts to explore how data engineering can unlock the full power of intelligent applications. Learn how to reduce latency, scale pipelines, and enable real-time decision-making. 🎥 Watch the on-demand webinar hosted by Database Trends & Applications now: https://bit.ly/4esNOTg #DataEngineering #AI #ML #Analytics #RealTimeData #DataOps #CData #DBTA

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As Databricks adoption accelerates, platform leaders face mounting pressure to deliver reliable performance, predictable costs, and scalable data operations. But with a fragmented vendor landscape and no one-size-fits-all approach, selecting the right observability solution can feel overwhelming. That's why we created the Databricks Data Observability Buyer's Guide - a clear, actionable framework to help you cut through the noise. 📥 Download your free copy today: https://lnkd.in/gayN9ui9 #Databricks #DataObservability #FinOps #AIOptimization #CloudCosts

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AI is shifting from single models to ecosystems of specialized agents. This research on Reinforced Agent Merging (RAM) highlights a critical reality:  If you merge RL-trained agents the same way we merged fine-tuned models, you destroy value. Why it matters:  • RL agents learn sparse, task-specific intelligence  • Naive averaging dilutes the very behaviors you trained them for  • “One model to rule them all” is no longer viable What RAM shows instead:  • Share common intelligence  • Preserve and amplify unique capabilities  • Combined systems can outperform individual specialists Impact for leaders and builders:  • AI platforms will be agent-based, not model-centric  • Competitive advantage shifts to orchestration and integration  • The hardest problem is no longer training models, but merging intelligence without loss This is where AI strategy meets product leadership. If you are still designing around a single assistant, you are already behind. #AI #ArtificialIntelligence #AgenticAI #ReinforcementLearning #GenAI #AIArchitecture #AIProductManagement #AILeadership #EnterpriseAI https://lnkd.in/ed7pTrsD

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