Meetups & Events

Pete George led a session about enabling AI safely, responsibly, and efficiently at scale. Going beyond prompting, we dove into various levels of control — examining observability, monitoring, and the necessity for human-in-the-loop review.

We kicked off our multi-session hands-on Kubernetes series! Led by Kubestronaut Chase Christensen, Part 1 covered Kubernetes fundamentals (Pods, Jobs, Deployments), local cluster setup using Docker Desktop, and kubectl CLI tooling validation.

It was great to meet our new members! Topics included a "birds of a feather" conversation focused on how to talk tech with leaders and shared insights on building systems, change management, and navigating our new AI world.

Join us for a practitioner-focused talk on building insight centric, drift-resistant revenue forecasting models for retail businesses. We will cover the full modeling lifecycle, regression diagnostics, and establish ongoing monitoring frameworks.

We explored critical questions for contributors and builders alike: How do you know when to donate your valuable time to a project? And how does a project's fundamental organization dictate how far your contributions go?

Your domain-specific model achieves 85.3% accuracy, but production requires 95%+. What do you do? Discover how ensemble models and multi-source data synthesis can bridge the gap when single GenAI models fall short. Learn about the "judge pattern" that achieved 100% accuracy with explainability and cost-effectiveness.

Explore the challenges of deploying machine learning models to production environments and maintaining their performance over time. This session dives deep into model drift, monitoring strategies, and best practices for keeping your ML systems reliable and accurate.

Join us for the official launch of The Applied Machine Learning Collective of the Rockies! This foundational event brings together AI/ML practitioners from across the region to network, share ideas, and shape the future of our community.