Rick Perroset

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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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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No A/B test? No problem. Learn how Regression Discontinuity Design (RDD) can uncover causal effects in observational data by exploiting sharp cutoffs. Dive into the theory and a real-world e-commerce example in Alejandro Alvarez Pérez's newest article.

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In this hands-on tutorial, Gavi walks you through how to create a powerful AI assistant using Databricks Agent Bricks - without writing a single line of code. Whether you're a marketer, developer, or data enthusiast, this video shows you how to connect your own knowledge base and deploy a smart agent in under 10 steps.

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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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We’re seeing a lot of interest in how Snowflake Semantic Views can streamline metric governance and reduce duplication across BI tools. A great example of this is the new integration between Sigma and Snowflake Semantic Views, highlighted in a recent blog post by Ben Weiss: https://lnkd.in/g72bK8Tk With this integration, Sigma can now query Semantic Views directly — meaning the metrics, dimensions, relationships, and business logic defined in Snowflake become immediately available in Sigma’s workbook experience. No re-modeling, no re-defining metrics, and no risk of drift between tools. Because Semantic Views live in the warehouse, teams can version-control their semantic definitions, apply governance consistently, and expose a single source of truth across the organization. Sigma simply reads what’s defined in Snowflake, giving analysts and business users a consistent, governed layer without needing to manage a separate semantic model inside Sigma. We’re seeing this resonate with customers who want to consolidate metric logic, keep everything close to the data, and simplify how teams build dashboards and explore data. It’s a clean architecture: define once in Snowflake, consume anywhere. Great write-up by Ben — worth a read if you’re exploring how to operationalize your semantic layer across BI tools. Links to relevant docs in comments.

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