Community Stories

Problem

Chase is a solutions engineer focused on helping technical teams get revenue-generating wins. His job is part technical solutions expert, part business driver.

Chase knew his solution, but wasn't going to help teams drive value or real outcomes flying blind. He believes that understanding where a solution fits best builds trust and ensures he is helping teams long-term. As a startup solutions engineer, Chase is constantly evaluating whether the "new way" he's proposing is truly the right one.

He needed users who had used other solutions—so he could learn from them, partner with them, and understand what they do well—to better set everyone he engages with up for success. With so many ways to solve problems and so many tools worth learning, Chase wanted real-world validation—not just vendor marketing.

How Did You Discover the Community?

Chase found the community on Meetup and decided to attend. He had just moved to Colorado and was incredibly excited to meet new technical folks.

How Did the Community Help You?

Chase learned a TON from the community. He learned about how folks understand Open Source, what data scientists care about, and met several people he would later build a new community with to serve the one that supported him.

What Did You Contribute Back?

Chase became an organizer for the community and began leading discussions on Open Source, Kubeflow, and Kubernetes. He even flew in his community mentor (Amber) to give an Open Source and Kubeflow talk to the group!

What Happened As a Result?

Chase developed a deep passion for connecting with others. He often says, "Community and solutions is a contact sport. Let's be in front of people and build real relationships." He also co-founded the AMLC of the Rockies community to help others in Denver get jobs, contribute to projects, and build meaningful connections.

Future Contributions or Goals

Chase hopes to keep serving the community and develop more offerings to help members get what they want out of AMLC.

Problem

Pete is a data science consultant who spends his days helping customers become stronger practitioners while managing a team of data scientists. His work spans data engineering, model development, and data collection. But Pete recognized a gap—he lacked a deep understanding of model deployment and the operational side of the ML lifecycle. He wanted real-world examples, not vendor pitches, and hoped to find them in a casual, authentic setting—like a happy hour.

Community Solution

Pete found an AI/ML meetup focused on the missing pieces of the ML lifecycle—especially ModelOps. There, he connected with ML engineers and DevOps professionals who had hands-on experience deploying models at scale. These new connections shared practical advice, hard-earned lessons, and tooling insights that helped Pete see the broader picture.

Contribution & Growth

Inspired by what he learned, Pete shared his own experiences as a data scientist, highlighting common pain points his team faced. He realized that many in the community struggled with the same issues—often solving them via command-line tools and ad hoc workflows. Pete began contributing regularly, offering solutions and driving outcome-oriented discussions. His perspective grounded in real-world delivery earned him trust. Over time, he became a go-to voice in the community for bridging the gap between model development and deployment.

Outcome

Pete became more than a participant—he became a leader. He now advocates for thoughtful ML lifecycle strategies both inside his organization and out in the field, confident that he's backed by a community of practitioners solving real problems, not just theory.

Problem

Vince was a full-stack backend engineer juggling API development, frontend integration, and managing technical debt. With a newborn on the way and machine learning gaining momentum, he felt a mix of boredom and anxiety—concerned about staying relevant in a rapidly evolving tech landscape. Curiosity sparked his interest: Was machine learning just hype, or was there real value? He didn't want to pivot blindly—he needed a way to explore ML in the real world without overcommitting. What really intrigued him was the "Ops" side—the connective tissue between software engineers and data scientists.

Community Solution

Vince discovered the AMLC community through Meetup—and noticed that Pete the Data Professional was now an organizer. Having attended meetups before, Vince appreciated the format: low-pressure, high-impact. He joined to listen, learn, and contribute when he could.

Community Contributions

Vince quickly connected with the community's energy. Inspired, he explored how machine learning could be applied to agriculture—an area he deeply cared about. This hands-on curiosity led him to submit a talk to the community. Pete formally connected with Vince around this time. Vince's presentation sparked conversation and collaboration, sharing GitHub repositories and real code to help others get started—turning inspiration into action.

Outcome

By contributing knowledge and starting conversations, Vince found more than just answers—he found belonging. He became an engaged community member, with renewed relevance, confidence, and the sense that he wasn't just learning—he was shaping what came next.

Problem

Ben is a Multimedia Lead and Educator who realized that in the age of AI, telling the story wasn't enough—he needed to understand the engine driving it. Working in creative media, Ben saw a ceiling approaching. He possessed elite storytelling and video production skills, but recognized a massive gap in the market for people who could both communicate complex tech and execute it.

He didn't want to just make videos about AI; he wanted to understand the under-the-hood mechanics of MLOps and Kubernetes to create educational content that actually resonated with engineers. He needed a way to fast-track his technical literacy without going back for a four-year CS degree.

How Did You Discover the Community?

Ben found the community through the local Denver tech Meetup scene. He was looking for spaces where the hard tech conversations were happening—places where he could listen, learn, and eventually translate those high-level concepts into accessible content.

How Did the Community Help You?

The community was the catalyst for Ben gaining a new role. By rubbing shoulders with practitioners, Ben was able to validate his learning path, ask the "dumb questions" in a safe space, and gain the technical confidence to pursue roles that required adjacent technical skills to truly compete. It bridged the gap between his creative background and the technical requirements of the modern corporation.

What Happened As a Result?

Ben successfully gained a new role with the skills and network acquired from the community. He uses his hybrid skills to tell stories and move forward tech-centered projects for his organization.

Future Contributions or Goals

Ben aims to keep the AMLC of the Rockies a genuine, practical space for professional growth. He wants to help others, specifically those from non-traditional backgrounds, find their footing in the tech industry by providing the same mentorship and open doors that helped him succeed.