Rafal Orlowski

For Pierre-Claude G., Data Scientist at McGill, the real challenge with AI is understanding the tradeoff between cost, adoption, and real value. What stood out to him about Dataiku is how it acts as a workflow orchestrator, bringing data prep, modeling, connectors, and deployment into one place so teams can involve business users from the start and move from MVP to production quickly. That simplicity makes it easier for AI projects to actually get adopted. Watch the full interview below to see how Pierre thinks about turning AI projects into real-world solutions. #DataPrep #AI

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Introducing: Getting Started with TellR AI-Powered Slides from Your Databricks Data How much time does your team spend manually pulling data from Databricks and assembling slide decks? What about the governance risk of copy-pasting enterprise data into external AI tools? TellR solves both problems. It's an open-source Databricks App created by Robert Whiffin, along with Tariq Yaaqba and Puneet Jain, that generates interactive, presentation ready slide decks from your live data using natural language all within your Unity Catalog governance boundaries. I've published a full written tutorial and video walkthrough covering the end-to-end setup: - Exploring the Bakehouse sample dataset - Installing TellR and verifying the Lakebase - Creating a Genie space for your data - Building custom slide styles for brand consistency - Configuring profiles that bundle data source, style, and prompt - Generating and refining slides conversationally - Exporting to interactive HTML (with Chart.js) or PDF The key takeaway: your data never leaves your workspace. TellR queries through Genie spaces, ensuring Unity Catalog permissions apply automatically. No data leaks, no governance gaps. Links below #Databricks #DataEngineering #AI #UnityCatalog #OpenSource #DataGovernance #Presentations #GenieSpaces #DatabricksApps #EnterpriseAI

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🔎 What is Ontology? And why it's the key to smarter AI? Knowing the connections between data is as crucial in complicated situations as the data itself. By defining and structuring those relationships, ontologies facilitate deeper, more insightful understanding. 👀 Check out our infographic to see how ontology plays a key role in powering AI-enabled Digital Twins and unlocking operational intelligence. #Ontology #AI #DigitalTwins #DataStructure #SmartData #Entopy #OperationalIntelligence

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Is your organization still struggling with inconsistent data and mismatches between source and target systems?   Persistent’s Data Observability Accelerator on Databricks is transforming how enterprises modernize data quality, freshness, and reconciliation. Leverage an AI-driven profiler to automatically generate data quality rule recommendations for your Lakehouse environment.   Discover how Persistent and Databricks are enabling seamless data observability: https://lnkd.in/dQgY7QyM Bidish Sarkar, Sameer Dixit, Brij Gupta, Moitreyi Nayek, Siddhant Dwivedi    #LegacyModernization #GenAI #DataObservability

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Just returned from ICML 2025 in Vancouver, one of the world’s premier machine learning conferences, where I had the privilege of presenting our work on Differentially Private Boxplots. Our core message: Privacy is not just about the algorithms, but about what you can see and interpret. https://lnkd.in/eaif-3b7 It was an inspiring and action-packed week, offering not only groundbreaking research but also a glimpse into the present state and future direction of the field. Some key takeaways from the conference: - AI systems often develop internal models of the world that differ from human expectations. Understanding this mismatch is essential for improving reliability. - The legal landscape around generative AI is still in flux, especially regarding data usage and copyright. Clear frameworks are urgently needed. - As AI becomes more integrated into society, alignment with human values must account for the fact that those values are constantly evolving. - Choosing what AI should optimize for is as critical as how it optimizes. Objective design is foundational across all applications. - End-to-end machine learning systems are now enabling closed-loop optimization and accelerating discovery in science and engineering. #ICML2025

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Analytics Governance isn’t optional in the Public Sector. It’s urgent. From misconfigured dashboards to manual QA bottlenecks, public agencies face mounting pressure to deliver trustworthy data analytics. In a world where data drives decisions and public trust, governance at the Analytics layer is no longer a nice-to-have. 🔍 Discover the 4 key reasons why Analytics Governance must be part of every public agency’s strategy, and how Wiiisdom helps make it happen. 📖 Read the full article at https://lnkd.in/ehpjvPXD #AnalyticsGovernance #governance #decisionmaking #PublicSector

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Strong Databricks governance is not just about tools. It is about the people, processes, and ownership that keep your data platform secure, scalable, and trusted. As organizations invest in lakehouse architectures and AI, governance gaps are becoming a real risk, from inconsistent reporting to security exposure. The difference between success and failure often comes down to having the right talent in place to design, implement, and manage governance effectively. If you are building or scaling on Databricks, this is where to start. Read the full blog: https://lnkd.in/eUaxPem9 #Databricks #DataGovernance #DataStrategy #AI #DataPlatforms #TechTalent #DataEngineering #CloudData #Analytics #Hiring #RevInsights

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What an incredible week at KDD 2025 in Toronto! The energy, innovation, and depth of discussion were truly inspiring — and it was great to see some old friends and meet new researchers. Here are my key takeaways: 🔹 LLMs & Generative AI — Now With Purpose The focus has shifted from simply building GenAI apps to tackling deployment, efficiency, security, and ethics head-on. 🔹 Data Quality Takes Center Stage The new Datasets & Benchmarks track highlights the importance of high-quality, accessible, and ethically developed datasets for reproducible and trustworthy AI. 🔹 Graph Learning & Time Series Make a Comeback Exciting advances in GNNs and sophisticated time series techniques—often fused with GenAI (I’ll share more at my AIAI talk later this year). 🔹 Applied Data Science is Redefining the Field This year’s Applied Data Science track delivered groundbreaking research with real-world, industry-scale impact. 📚 Interesting Tutorials I Attended/Reviewed Relational Deep Learning (Stanford) — https://lnkd.in/e3q_5ies Training Industry-Scale GNNs with GiGL (Snap) — https://lnkd.in/ecmSGtTY Unifying LLMs & Knowledge Graphs for Medicine & Healthcare — https://lnkd.in/e5dYAknH Graph Foundation Models — https://lnkd.in/epwhBRaS Graph Prompting for Graph Learning Models — https://lnkd.in/ef2NTFfn 🏆 KDD 2025 Awards (Links to Papers) Best Paper – Research: Improving Group Robustness on Spurious Correlation via Evidential Alignment — https://lnkd.in/eFBkhqk2 Runner-Up – Research: Monitoring Robustness and Individual Fairness — https://lnkd.in/eXdetQTA Honorable Mention – Research: SIGEM – A Simple yet Effective Similarity-based Graph Embedding Method — https://lnkd.in/eSgmgAsP Best Student Paper – Research: Taming Recommendation Bias with Causal Intervention on Evolving Personal Popularity — https://lnkd.in/eGfHc_ra Best Paper – Applied Data Science: Evaluating Decision Rules Across Many Weak Experiments — https://lnkd.in/e_NgXXim Honorable Mention – Applied DS: Web Scale Graph Mining for Cyber Threat Intelligence — https://lnkd.in/ecys5-ye Best Paper – Datasets & Benchmarks: When Heterophily Meets Heterogeneity — https://lnkd.in/eict3wnA Audience Award (Aug): LLMLight – Large Language Models as Traffic Signal Control Agents — https://lnkd.in/e_Zw29W6 Audience Award (Feb): Theory Instead of Experiment (TIE) – A Creator Valuation System at Tencent — https://lnkd.in/eK45HHZe #KDD2025 #DataScience #AI #LLMs #GenerativeAI #ResponsibleAI #MachineLearning #GraphML #FidelityAssociate

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Palantir Technologies recently released a new Foundry update that lets you build directly on your source tables, removing the need for manual syncing. As Ontologize’s Ben Thomas explains, most teams used to have to move data into Foundry before they could start modeling. With this update, you can create ontology object types backed by virtual or iceberg tables, so your data stays where it already lives, whether that’s in Snowflake, S3, or another external system. It’s a small change that removes hours of setup and makes it easier to model directly on your source systems. Try it out, and let us know what you think!  

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Last week, I was at the #HintonLectures in Toronto, listening to #GeoffreyHinton and other experts talk about AI safety, alignment, and what’s coming next. Two days later, Anthropic revealed that a state-linked group used an AI model to carry out the majority of a cyber-espionage attack autonomously. AI is moving faster than our safety frameworks, and we need to catch up. Hinton’s point was simple: systems don’t need to be programmed with “bad intentions” to develop power-seeking behaviour. They discover it because it helps them achieve their goals. The Anthropic incident was a pretty good demonstration of that. The people closest to #AIsafety aren’t giving up on AI. They’re pushing for safer systems and better guardrails. The rest of us have a role too. We can ask questions, support better policy, and stay engaged. AI isn't slowing down, but with real leadership, we can influence the direction it takes. In this week's newsletter, I write about where this leaves us and what we can push for to protect ourselves from bad actors using AI. Link is in the comments.

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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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Databricks engineers share how they scaled database reliability using big data analytics tools like Databricks, shifting from reactive monitoring to proactive scoring to enforce best practices!

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Let's try this again 😆 Why Ontology Pays Off When your data fits together, everything gets easier. With BrickX, Switch clients are seeing: 🚀 Faster deployments 📉 Less manual engineering 📊 Consistent, validated data across portfolios What once took weeks of mapping now takes hours. Ontology doesn’t just organize data — it accelerates performance, enabling insight at scale. #BrickX #SmartBuildingResults #DataDriven #SwitchAutomation

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We’re back with another episode of Meet the Minds! This time, we feature Dwight S., Data Engineer at RBC. Dwight shares how he converted his Business Analyst co-op into a full-time role and later advanced to Data Engineer, offering valuable insights from his journey in the data field. Follow the Humber Data Analytics Club for more stories and career journeys from the data & analytics community. Watch the full video, and feel free to like, comment, and share. #MeetTheMinds #DataAnalytics #CareerJourney #HumberPolytechnic #RBC #DataEngineer

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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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Dynamically generated, Ontology-powered fleets of agents. At DevCon 5, Palantir Product Manager Ben Radford walks through the architecture of AI Hivemind — covering the capabilities and tradecraft that enable the creation, orchestration, and optimization of specialized agent teams.

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