Annie Flippo

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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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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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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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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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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Legacy semantic models were never built for the velocity and complexity of AI-driven analytics. They assume fixed schemas, predictable joins, and a handful of BI queries, not hundreds of dynamic, multi-domain requests from copilots and agents. AtScale’s AI-powered modeling engine changes that through an adaptive feedback loop. It continuously analyzes warehouse metadata, query logs, and lineage to infer semantic relationships and hierarchies automatically. When candidate metrics or joins are discovered, they’re surfaced for human validation, accepted, rejected, or refined, and those outcomes feed back into the model’s learning system. All of this runs on AtScale’s composable semantic architecture, governed through Semantic Modeling Language (SML). Every suggestion, update, and approval is version-controlled, reversible, and fully auditable. It’s modeling that scales with your data, not against it. 🔗 Learn more:

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"All-in-One" Suite 🆚 "Best-of-Breed" Approach Most organizations aren’t 100% on any single platform. Heterogeneity is the norm. That’s why metadata freedom is becoming a critical architectural discussion. 👉 Full read: https://lnkd.in/gHnrPvxd In this article, we break down: • why vendor lock-in matters more at the metadata layer • how multi-engine environments change governance • the tradeoffs between open catalogs and vertically integrated platforms This topic affects every engineering team operating across Spark, Trino, Flink, warehouses, vector stores, and AI systems.

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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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Excited to be hosting a free webinar with NACM (National Association of Credit Management) on AI prompt engineering for modern credit teams Tuesday, March 31 at 3:00 PM ET. The session is based on a whitepaper Handle and NACM co-developed: "Turning One-Off AI Chats into Reliable Credit Workflows." Here's the thing...most credit professionals are already using ChatGPT or Claude. They open a tab, ask a question, get an answer that ranges from useful to generic, and close the tab. Nothing saved. Nothing repeatable. In this session, I'll walk through: ❓ The four-question framework that turns generic AI output into near-ready drafts 📄 Six named prompt patterns mapped to everyday credit tasks (collections, financial analysis, contract review, disputes) 📅 A 90-day plan for building a shared prompt library across your team (no IT involvement, no new software) If you manage credit risk, write collection letters, review contracts, or prepare escalation memos...this is practical, immediately usable content. Free Registration: https://lnkd.in/gxEFj8Ac

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