Ryan Chiang, Ph.D.

Integrating large language models and powerful algorithms into your workflows is a massive undertaking. All that hard work can be undermined if the outputs fall short, quickly eroding stakeholder interest and trust. That's why effectively evaluating LLMs and algorithms is crucial. From initial prototypes to production, and through continuous experimentation and benchmarking, mastering evaluation ensures these advanced tools truly deliver on their promise. #LLM #Algorithms #AI

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🚀 Ready to elevate your AI/ML strategy? CData's Jerod Johnson joins industry experts to explore how data engineering can unlock the full power of intelligent applications. Learn how to reduce latency, scale pipelines, and enable real-time decision-making. 🎥 Watch the on-demand webinar hosted by Database Trends & Applications now: https://bit.ly/4esNOTg #DataEngineering #AI #ML #Analytics #RealTimeData #DataOps #CData #DBTA

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Ignite Digital is a proud Snowflake Partner. Our certified team leverages Snowflake’s AI Data Cloud to empower DoD organizations to unlock the full value of their data and gain a decisive tactical edge. Crucially, Snowflake’s AI Data Cloud and DoD IL5 authorization provide the security, flexibility, and resilience needed for defense operations. 🔗 Learn more: https://lnkd.in/gDwYZM7J #WeAreIDS #Snowflake #DataCloud #AI #DoD #IL5 

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This foundational checklist from Augusto Rosa’s Snowflake Architecture Series is a must-read. Whether you're new to Snowflake or scaling your cloud data platform, this checklist breaks down the essential building blocks—like identity and access, cost governance, and data modeling—that every data team needs to get right. Set yourself up for long-term success with Snowflake—this is the perfect place to begin. https://lnkd.in/eYP8jZ2s

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Strong example of what responsible AI done right actually looks like. This piece by Lisa Schneider highlights why the Team Select and Cuesta Partners Silver Anthem Award matters beyond the trophy. This is not AI theater. It is applied, operational, and designed for real-world adoption in healthcare. A few things that stood out to me: • Data first, always. The AI model succeeded because the data foundations were built correctly first. Dashboards, business data models, and shared definitions delivered value immediately and reduced downstream risk. • Problem-led AI, not tech-led AI. The team resisted the urge to boil the ocean. They focused on a narrow, high-impact use case where early signals could genuinely change patient outcomes. • Interpretability over black boxes. Risk scores alone are useless in clinical environments. Surfacing contributing factors made the model trustworthy and actionable for care teams. • Workflow is the product. Adoption was designed, not assumed. Clear operating models, clinical co-design, and on-site alignment made the difference between a model that exists and one that gets used. This is the kind of AI story Tech leaders should be studying. Not because it is flashy, but because it is repeatable. Build the data muscle. Pick the right problem. Design for humans. Then scale. Awards are nice. Fewer hospitalizations and better patient outcomes are better. Well worth the read to see how AI that actually improves lives!

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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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