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Data architect skilled in Snowflake, SQL, Python, dbt, BigQuery, Power BI, Azure, and…
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Kaitlyn Wells
Snowflake • 2K followers
Running inference on R models shouldn’t require moving data out of Snowflake. I wrote a step-by-step guide on deploying R models in Snowflake’s Model Registry and running inference with Snowpark Container Services, helping you avoid unnecessary data movement and egress costs. Check out my blog ➡️: https://lnkd.in/erhrhMAk
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Dave Fox
Focus GTS • 21K followers
One pattern keeps showing up in conversations about the Adobe talent shortage. Companies are trying to hire one person to do the work of four disciplines. Look at a 𝘁𝘆𝗽𝗶𝗰𝗮𝗹 𝗔𝗘𝗣 𝗼𝗿 𝗥𝗧𝗖𝗗𝗣 job description today. 𝗜𝘁 𝗼𝗳𝘁𝗲𝗻 𝗲𝘅𝗽𝗲𝗰𝘁𝘀 𝗼𝗻𝗲 𝗽𝗲𝗿𝘀𝗼𝗻 𝘁𝗼 𝗵𝗮𝗻𝗱𝗹𝗲: • Identity resolution architecture • XDM schema design • Data engineering pipelines • Audience activation strategy • Marketing orchestration 𝗧𝗵𝗮𝘁’𝘀 𝗻𝗼𝘁 𝗼𝗻𝗲 𝗿𝗼𝗹𝗲. That’s four different skill sets. So organizations s𝘀𝗲𝗮𝗿𝗰𝗵 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘂𝗻𝗶𝗰𝗼𝗿𝗻… …and the job sits open while projects stall. But 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 isn’t just the talent shortage. It’s 𝘁𝗵𝗲 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗼𝗳 𝘁𝗵𝗲 𝘄𝗼𝗿𝗸. 𝗠𝗼𝘀𝘁 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗮 𝗵𝗶𝗿𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺... 𝘁𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗮 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. The teams making real progress with AEP and RTCDP aren’t waiting for a perfect hire. They’re assembling cross-functional capability instead. 𝗧𝗵𝗮𝘁’𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝘆 𝘄𝗲 𝗰𝗿𝗲𝗮𝘁𝗲𝗱 𝗡𝗮𝘃𝗶𝗴𝗮𝘁𝗼𝗿. Navigator gives organizations access to senior Adobe specialists across the stack so work can move forward without waiting months to hire a single unicorn role. Because in this market, 𝘄𝗮𝗶𝘁𝗶𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝗵𝗶𝗿𝗲 𝗶𝘀 𝗼𝗳𝘁𝗲𝗻 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗼𝗽𝘁𝗶𝗼𝗻.
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Quartr
36K followers
Quartr API datasets are now available via Snowflake. Access structured earnings data – live audio, real-time and historical transcripts, filings, slide decks, and event summaries. Pull Quartr data directly into your existing Snowflake workflows and augment your datasets. No new pipelines, no separate API integration. Build with data that drives decisions.
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Rockford Yost
Yum! Brands • 626 followers
I’m excited to be speaking at #NVIDIAGTC on what it takes to turn general-purpose models into domain-expert agents that can perform reliably in production. Alongside Santiago Pombo (NVIDIA) and Arijit Sengupta (Aible), I’ll be sharing how Yum! Brands is optimizing agentic tool-calling for high-volume use cases—using Small Language Models (SLMs) and synthetic reasoning traces to improve disambiguation, execution quality, and latency at scale. This session will dive into how enterprises are moving beyond prompt engineering and toward evaluation-led, data-driven customization loops. We’ll explore real-world approaches to specializing open models for domain-specific tasks, from Txt2SQL in regulated environments to latency-sensitive commerce use cases at Yum. If you’re building agentic AI systems and thinking deeply about model specialization, tool use, evaluation, and scalable production design, this is a session worth catching. ⬇️ Building Domain-Expert Agents: How to Optimize Txt2SQL and Tool-Calling with Open Models [S81707] https://lnkd.in/gfKr6XUy
1 Comment
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MLtwist
995 followers
Agentic AI starts with structure. Before an AI system can plan, reason, or act autonomously, it needs structured, auditable, multimodal training data. That starts with your pipeline. Our whitepaper with Google breaks down the data architecture powering the next generation of agentic AI, where data readiness determines autonomy. 📥 Get AI Data Pipelines for Machine Learning Models: https://lnkd.in/gZr-ZTPe #MLtwist #AgenticAI #DataInfrastructure #AIready #GoogleCloud
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