Charlene L.

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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You trained the model. You trusted the output. But can you trace the data that led to the decision? 👀 AI in analytics isn’t dangerous because it’s wrong. It’s dangerous because it’s convincing, even when it’s wrong. 📰 This article breaks down the real risks of AI-powered insights and how to design for trust with traceability, governance, and human oversight. Read it here: https://lnkd.in/eKuQnv7J

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The Databricks Startups team answers founders’ most common AI questions—where to start without hype, how to stay compliant with sensitive data, what to build versus buy, how to cut hallucinations, and how to keep costs in check. 💡 Get step‑by‑step starting points, governance patterns for sensitive data, a pragmatic build‑versus‑buy lens, tactics to curb hallucinations, and cost controls that scale. 🔗 Full post: https://lnkd.in/ehRa3Yp5 #Databricks #DatabricksForStartups #AI #GenAI #MLOps

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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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“It’s not whether you can build the system. It’s whether you can build the right one.” Modern tooling makes it easy to stand up production-grade data systems. The harder problem is defining shared concepts across an entire firm and ensuring those definitions hold as the organization grows. In our latest Substack post, we write about how data strategy has shifted from a technical challenge to a business one, and what that means in practice. 🔗 https://lnkd.in/ej3caCXp

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Curious how your Databricks data estate is really performing? Download a free sample of Unravel's Databricks Health Data Estate Report and see the kind of deep insights you can unlock: 📈 Performance insights to speed up jobs and workflows 📈 Productivity boosts with top improvement areas—no log diving required 📈 Savings projections for clusters and pipelines 📈 SLA attainment metrics to reduce pipeline delays 📈 Job health visibility to fix frequent failures fast Start uncovering hidden opportunities for efficiency: https://lnkd.in/gJ7qu4Nu #Databricks #CloudFinOps #DataObservability #PerformanceTuning #DataEfficiency #UnravelData

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Data silos are one of the biggest roadblocks to efficient analytics and AI workflows. MinIO’s new Aistor Table Sharing is designed to tear down those barriers by enabling direct, on-premises data access from Databricks without copying or duplicating data. Key takeaways: • Aistor Table Sharing lets Databricks query on-prem MinIO data directly, eliminating data movement. • This approach reduces complexity, storage sprawl, and latency for hybrid and on-prem use cases. • By keeping data in place while supporting modern analytics and AI consumption, organizations can accelerate time to insight. Full story: https://lnkd.in/e4mh22md #DataManagement #HybridCloud #AIAnalytics MinIO

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*What is the need for Palantir Foundry?* Fundamental Problem with standard platforms like Snowflake, Databricks, and Cloudera for Data Production and Data Pipelines: fragmentation is the core issue when using disconnected tools. While each tool handles a specific piece of the business, they do not provide a full picture. This makes it difficult for a manager to instantly connect a tenant's work order with relevant financial or scheduling data Foundry's main advantage is that it combines storage, transformation, orchestration, and application building into a single governed environment, whereas other solutions require stitching multiple separate tools together

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