Chase C.

• Founded and registered The Applied Machine Learning Collective of the Rockies in Colorado. • Developed a comprehensive community handbook to guide members and streamline operations. • Integrated collaboration systems to facilitate effective communication among AI/ML professionals. • Hosted weekly meetings to foster engagement and build a robust network across the region.

https://github.com/kubeflow/community/blob/master/KUBEFLOW-OUTREACH-COMMITTEE.md The Kubeflow Outreach Committee (KOC) is a committee dedicated to fostering growth, engagement, and community outreach for the Kubeflow project. It will focus on activities that promote Kubeflow to new and existing users, contributors, and stakeholders while building an inclusive, vibrant, and diverse community within the broader AI/ML ecosystem.

As a Staff Solutions Engineer at TileDB, I: * Advanced multi-modal array technology: Led the implementation of TileDB's capabilities to store and retrieve diverse data types (images, text, structured data) in a unified format, optimizing workflows across biotech and music licensing. * Built gene therapy-focused RAG system: Developed a system using BioMinstral-7B and BioBERT for PubMed article ingestion, enabling fast, efficient retrieval across large biomedical datasets. * Architected scalable data pipelines: Designed end-to-end pipelines that processed thousands of biomedical articles, leveraging Kubernetes to achieve optimal parallelism, reduce ingestion times, and maximize resource efficiency. * Improved system scalability: Enhanced MariaDB installation processes for better scaling under high loads, significantly improving client production environments. * Pioneered vector storage for fast retrieval: Integrated TileDB’s vector search capabilities to enable efficient similarity searches, improving data access and processing. * Focused on future-proofing and open-source contributions: Applied popular tools like Hugging Face for scalable embeddings, ensured implementations were robust, and contributed to open-source projects that pushed data interaction boundaries.

* Built the “Introduction to AI/ML Toolkits with Kubeflow” Course, educating a broad audience on best practices for deploying and managing Kubeflow. * Promoted Kubeflow adoption by running workshops at Linux Fest and working the Kubeflow booth at Kubecon.

Continuation of my role at Arrikto through an “acquisition hire” Served as a globally scoped, field-facing Staff Field CTO responsible for enabling and ensuring execution across ML/AI product engagements—initially focused on Kubeflow and HPE Ezmeral’s enterprise AI offerings. Key Contributions: Developed a comprehensive, multi-day enablement session (10+ hours, 300+ slides) covering the enterprise AI value proposition, ML platform strategy, workload orchestration, operator/controller lifecycle patterns, and how Kubeflow addresses these challenges to drive ROI across AI maturity levels. Delivered to both sales and engineering audiences. Designed and built 10+ hands-on labs and demos aligned with the above content to upskill pre- and post-sales teams on applied usage of Kubeflow and related tooling in customer scenarios. Collaborated with fellow global Field CTOs to refine go-to-market strategies and improve field SE outcomes across regions. Engaged with customers in pre-sales conversations to assess MLOps maturity, align on goals, and define success metrics. Joined a specialized product enablement task force to produce technical content, how-tos, competitive positioning, and feature overviews for internal training and external engagement. Presented at customer briefings, partner events, and virtual workshops on topics such as: Reproducibility vs. Replicability in AI Distributed Computing for ML Retrieval-Augmented Generation (RA

As a consultant for this early stage DataPrepOps startup I: * Successfully deployed, managed, refactored, and iterated on Active Learning and DataPrepOps workflows. * Collaborated with cross-functional teams, including data scientists and engineers, to integrate active learning strategies into their training pipeline. * Worked with Alectio founder and now Deep Mind leadership Jennifer Prendki to understand and operationalize DataPrepOps strategies

Presales, post sales, and support resources at an early stage ML/AI and Kubernetes oriented startup focused on distributing data intensive workloads at scale. As a "many hatted" resource I : * Developed prototypes for our managed Kubeflow offering leveraging ArgoCD, Crossplane, Helm, and Kubernetes * Developed blogs and tutorials in order to to discuss bleeding edge ML/AI topics * Spoke at conferences on the topics of cross team collaboration and Cloud Native Computing Foundation projects such as Kubernetes and Kubeflow (Kubecon NA AI day 2022) * Engaged in pre-sales and post-sales conversations around distributed workloads and platform architectures (including language model tuning on Kubeflow) * Was responsible for troubleshooting customer environments on GCP as well as AWS running various Kubernetes and Kubeflow distributions.

https://omscs.gatech.edu/specialization-interactive-intelligence