Douglas Pestana

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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We're facing more and more questions about the use of synthetic data in market research. I'd recommend this blog post, which really resonated with me. So much of the conversation is "how closely can this replicate human data?" But there are so many other considerations researchers should be thinking about. https://lnkd.in/eSQfSTPj

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Last week, I spoke at the Data Behind the Scenes conference #DataBS It's a conference run by Randy Au for data practitioners who are tired of polished case studies that skip over the actual work. Instead you get honest takes on project realities: the messy bits, the trial and error, the things that go wrong. I spoke about the 10 red flags that your multi-market research data went sideways. We've all had that moment, staring at survey results that look technically correct but feel fundamentally wrong, then having to explain why three countries' worth of data is unusable. Links to my talk, as well as the full playlist, are in the comments

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You should be rewriting your prompts We talk about overfitting models, but never overfitting prompts to models. Models have specific formatting preferences - OpenAI favors markdown while Anthropic uses XML - and might weight different parts of the prompt differently. Newer models won't necessarily perform better out-of-the-box until you do this testing and optimization. https://lnkd.in/eNq6DqXJ

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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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🚨 What’s the one skill top data teams are rushing to master now? 🤔 Not AI. Not data science. It’s data observability—and you can get certified for FREE. Why? Because broken pipelines and untrusted data are killing AI before it starts. Built by Acceldata’s team, trusted by global leaders, this course helps you 👇 : ✔ Build AI-ready pipelines ✔ Master governance and reliability ✔ Earn a LinkedIn-shareable certificate 🎓 Claim your free Data Observability Certification today: https://lnkd.in/gbZDFcPr 📖 Want to learn more? Read the blog → https://lnkd.in/gRiPkGwz #DataObservability #AIReadyData #DataLeadership #DataEngineering #DataGovernance #DataManagement, #Acceldata #FreeCertification #AIOps

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