Adam Stopek

Over 100 enterprises are already seeing real impact from the Databricks and Palantir partnership. With Palantir Technologies' world-class AI Operating System + the Databricks Platform and Agent Bricks, customers are moving from pilots to production-scale AI deployments faster than ever. Our partnership provides a secure, scalable architecture that enables teams to deploy joint AI workflows quickly and with confidence. See what else our customers are saying: https://lnkd.in/gjFEq2HW

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We have released TimesFM-2.5, the new leader in the GIFT-Eval on all accuracy metrics among zero-shot foundation models. This improved foundation model is now available on Hugging Face, with an upcoming release on GCP's BigQuery and Model Garden. This release represents a significant advancement. TimesFM-2.5 outperforms TimesFM 2.0 on leading benchmarks by up to 25%, while using half the number of parameters (200M). It also features a longer maximum context length of 16K. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗧𝗶𝗺𝗲-𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴? Time-series analysis is the process of studying data points collected over time to make predictions and identify trends. It is a critical tool for a wide range of applications, like: ➡️ Forecasting future product demand. ➡️ Tracking weather and precipitation ➡️ Optimizing supply chains and energy grids. 𝗪𝗵𝘆 𝗶𝘀 𝗶𝘁 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗶𝗻𝗴? Time-series forecasting is difficult because data patterns are often complex, can change over time, and are influenced by numerous factors. Developing a single model that can perform well across diverse datasets without being explicitly trained on each one has been a major challenge. 𝗧𝗶𝗺𝗲𝘀𝗙𝗠-𝟮.𝟱: 𝗔 𝗡𝗲𝘄 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿 𝗭𝗲𝗿��-𝗦𝗵𝗼𝘁 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 TimesFM-2.5 sets a new standard for a decoder-only foundation model trained on a large time-series corpus. 🤗 Leading Performance: TimesFM-2.5 holds the top position on the GIFT-Eval leaderboard for point forecasting accuracy (MASE) and probabilistic forecasting accuracy (CRPS). 🤗 Efficiency: The model's efficiency is a key feature, with a small parameter count that makes it practical for a wide range of production environments. 🤗 Longer Context: The increased context length allows the model to process more historical data, leading to more accurate forecasts. This work reiterates that building a single foundation model for time series forecasting is possible. Google Research keeps pushing forward the frontier of time series forecasting research. We are grateful to our community and customers who have provided feedback and deployed TimesFM in production. We are interested to hear more about how you are using TimesFM. Read more in our repository and see the leaderboard. GiFT-Eval: https://lnkd.in/dAwAcKA7 GitHub: https://lnkd.in/dWXH7BAm  Hugging Face: https://lnkd.in/dtx9iMHE To learn more about the foundational model read the Paper: https://lnkd.in/dRw3zzXT

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Demo: how to surface and use Unity Catalog Metric Views in Databricks AI/BI — to enable self-serve access to trusted business metrics. See how to set up central governance with row-level security and column masking, and more.

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It would've taken 25 years for this Snowflake customer to get 100% business data quality coverage. With Anomalo, Discover now configures unsupervised data quality monitoring within 10-15 minutes for each dataset. Learn how Discover’s data team flipped the script using Anomalo on Snowflake to scale trust across petabyte-scale data: https://lnkd.in/gPjxDCFH

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Attending Snowflake BUILD 2025 was such an incredible experience. It’s amazing to see how Snowflake is re-imagining the future of AI and data. Here were my top 5 takeaways from the conference: 1️⃣Building the agentic future: AI agents moving from concept to production. 2️⃣Cortex AISQL: natural language queries, no prompt engineering needed. 3️⃣ Cortex Agents: from queries to multi-step reasoning and automation. 4️⃣ Managed MCP Server: bridging AI tools through standardized connectivity. 5️⃣ Gen2 Warehouses: a data foundation built for real-time, enterprise-scale AI. Watch the full breakdown in my video 🎥 And if you’d like to learn more about Snowflake Intelligence, you can check out the link here: https://lnkd.in/gnRkJPv2 #SnowflakePartner #SnowflakeBUILD

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If you work with data, you’re probably familiar with the concept of data lineage: tracking where data comes from and how it moves through your systems. Column-level lineage at the column level takes that a step further by showing how each individual field is created, transformed, and used across tables, models, and dashboards. That level of detail is essential for debugging pipelines, managing schema changes, and making sure business metrics stay accurate as your data environment grows more complex. In this post, we’ll look at real-world data lineage examples from organizations across industries — including healthcare, construction, financial services, agriculture, and technology. Companies like HDC Hyundai, nib Group, Pennant Services, Bowery Farming, Xometry, AlphaSense, Block, and Faire are using lineage at the column level to: Cut data pipeline costs Speed up root cause analysis Improve data quality and trust Streamline compliance and governance Whether you’re exploring lineage tools or building out a data governance strategy, these stories show what’s possible when you have full visibility into how your data works at the column level. by Nicole Mitich at Select Star https://lnkd.in/gVeK9DBN

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Whether you believe Databricks is worth $100B or not, and whether you like what it offers or not, here’s the reality: Databricks has pushed all the major players to deliver more value out of the box. The days of stitching together a thousand different cloud tools are fading, except for when you need a very specialized tool. Databricks’ strategy of bundling many affordable tools in one platform has been a major driver of this shift. Again, whether you like them or not, every other platform is now fighting for market share by trying to look more like Databricks. This is good for customers.

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"We can just plug in the historical data right?" My head: "No, because it's captured differently with different segmentation, at a detail level we don't support through two different systems with inconsistent naming conventions and saved in CSVs on someones hard drive but we don't remember who it is, except for the weeks they went on PTO which are just missing and can't be replaced." My mouth: "We'll look into it."

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Today we're announcing that Semantic View Autopilot is generally available in Snowflake! When we GA’ed Cortex Analyst a year ago, we learned that the barrier to AI-powered analytics was never the LLM, it was data definition. When "Monthly Recurring Revenue" means different things to product and finance, AI agents inherit that inconsistency. No amount of prompt engineering fixes that. You need a governed semantic layer - but building one manually takes weeks. So we built a system that learns from how organizations actually use their data. SVA analyzes query history, Tableau dashboards, and trusted SQL to propose governed definitions automatically. Teams review and certify instead of coding from scratch. The more you use it, the smarter it gets. Since launching in preview, we've watched customers go from "I'll spend the next two weeks building a semantic layer" to "I had something working in my first session." Proud of the whole team that shipped this. Read the full announcement: https://lnkd.in/g7Dvhn9D Get started with the docs: https://lnkd.in/guMjU92w #Snowflake #AI #DataAnalytics #Cortex #SemanticView

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I've been lucky to learn from Jay at two separate jobs: great perspective on the value of a semantic layer in the post-AI era. "Most companies have 1,000s of tables, several with 100+ columns, and these tables are inter-related. Bringing a Semantic Layer to this high dimensional manifold is like bringing a label printer to a jungle. Now start by labeling each thing in the jungle. Then label the relationships between the things, and finally label all the interactions you might have with a thing or a relationship. This is something you can do, but it’s a very silly thing to do. And when a data company tells you that they sell such a label printer, you should imagine yourself stumbling around labeling things until the Earth crashes into the Sun."

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Data Commons is constantly evolving to make the world’s most critical public statistics more accessible. Today, we're accelerating the integration of global statistical data (GDP, population, etc.) with new open-source tools supporting SDMX 2.1 (Statistical Data and Metadata eXchange). https://lnkd.in/ekCUJh52 SDMX is a global standard for exchanging statistical data and metadata such as unemployment rates, GDP, and population figures, widely used by national statistical offices, central banks, and international organizations like the OECD, the World Bank, and the United Nations. -Key benefits now: Automatic download and transformation of SDMX sources for easy usage in Data Commons. -What's next? Our roadmap is focused on auto-schematization to fully automate the rest of the ingestion process with a goal of less manual work and more high-quality data. More info (with links to GitHub) in our post from Rohit Kumar and Sandeep Tuniki: https://lnkd.in/ekCUJh52 #DataCommons #SDMX #Roadmap #DataScience #DataEngineering

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Ready to move beyond ML experimentation? Our 6-Phase Implementation Playbook turns field-tested lessons into actionable steps—with specific timelines, deliverables, and success criteria for each stage. Get the complete roadmap from problem definition to production scaling: https://lnkd.in/dxPs9GZ3 #MachineLearning #MLOps #Implementation

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A quick update on #OSI (Open Semantic Interchange), a spec focused on making semantic definitions portable across data, AI and BI tools: we’ve published the first public Apache 2.0 version of the spec, an important step toward more open, shared semantics! We’ve also added a number of new partners to the OSI ecosystem: AtScale, Coalesce.io, Collate, Credible, Databricks, JetBrains, Lightdash, and Qlik. It's great to see continued momentum and collaboration. In our latest working group session we identified several key working groups that will focus on the continued evolution of the specification, including things like: Metrics Expression Language, Composability, Catalog Integration, Ontology Representation, Model Converters, and Developer Tools More context in the blog below, and a link to the repo is in the comments 👇 https://lnkd.in/gEaQXSME

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Yesterday I read this great article for the upcoming EMNLP about product review summaries from a few folks at Wayfair: End-to-End Aspect-Guided Review Summarization at Scale by Ilya Boytsov, Vinny DeGenova, Mikhail Balyasin, Joe Walt, Caitlin Eusden, Marie-Claire Rochat, Margaret Pierson Retailers: this is how review summaries should be done. Wayfair’s new paper shows an end‑to‑end, aspect‑guided approach that turns thousands of raw reviews into a short, trustworthy product summary shoppers can actually use. What’s novel for retail: - Evidence‑first UX: The model writes ~300–500‑character copy anchored to representative reviews. Shoppers can tap aspects like comfort or delivery to dive in. - Real‑time ops: Auto‑generate once a product hits 10 reviews and then auto‑refresh when new reviews grow enough. - Built for scale: Cached aspect mappings + sampling keep costs and latency in check without losing signal. - Proven impact: AB testing shows lifted ATC and CVR and cut bounce with no hit to revenue or page speed. - Open data: ~12m anonymized reviews across 92k products with extracted aspects + generated summaries available on Hugging Face. Open data is a key to comparing models across industries and use cases. Why it matters: A trustworthy, interpretable, continuously fresh review UX that boosts confidence and carts.

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