Artificial Intelligence

Join the Columbus AI community for our quarterly gathering — a casual, community-focused evening where everyone has a chance to share, learn, and connect. These open mic–style events give anyone in the community up to **5 minutes** to present a project, share a tool, pose a question, or offer a perspective on the evolving AI space. No slides required — just a welcoming space to exchange ideas and keep the local AI conversation moving. If you’d like to take the stage, message \*\*Chris (the organizer)\*\*with a **title and short description** of what you’d like to share. Whether you’re deep in the field or just getting curious, come connect with others building and exploring AI in Columbus. Sponsored by [Transform Labs](https://www.linkedin.com/company/transformlabs/) Sign up also accessible via [Transform Labs Luma](https://luma.com/transformlabshq)

Customer identity resolution becomes increasingly complex as organizations scale across multiple systems, regions, and data formats. Traditional rule-based approaches often fail to keep up with data variability, require constant manual tuning, and struggle with real-time processing needs. This session presents a practical approach to building a scalable identity resolution pipeline using AWS services and modern AI techniques. The architecture combines data ingestion through Amazon S3 and AWS Glue, transformation pipelines using Spark on EMR, and machine learning models deployed via SageMaker for entity matching and standardization. Graph-based relationship modeling is implemented using Amazon Neptune to improve resolution accuracy by incorporating household and shared attribute context. We will walk through how machine learning models can be used for name and address normalization, how intelligent blocking strategies improve matching efficiency, and how feedback loops can be introduced to continuously improve accuracy. The session also highlights how serverless components such as AWS Lambda can be used for orchestration and real-time processing. **SPEAKER BIO** Mosaic Syed is a Senior Data Engineering and Cloud Solutions Architect with over 20 years of experience designing and delivering scalable, secure, and high-performance data solutions across global enterprise environments. https://www.linkedin.com/in/mosaic-basha-syed-92300856 **CALL FOR SPEAKERS** Learn more: [https://www.awscolumbus.com/get-involved/](https://www.awscolumbus.com/get-involved/) **THANK YOU** *VEEAM* for hosting our meetup! To learn more about *Veeam*, please visit their website: [https://www.veeam.com/](https://www.veeam.com/) **DIRECTIONS** 8800 Lyra Dr #450 · Columbus, OH go to 4th floor. **Want to sponsor the pizza and/or bar tab?** Please contact me if you would like to sponsor this meetup's pizza and/or bar tab: angelo@mandato.com

**Important time note:** Please plan on arriving between 5:30 and 6:00 as the elevators lock after 6 and you'll need to message us and we'll need to come get you. The building address is 4450 Bridge Park The entrance is 6620 Mooney St, Suite 400 You will need to scan your ID at the door to get a visitor badge. **Abstract** TBD **YouTube Link** TBD

Practical AI for Power BI Developers A year ago, “agentic AI” was mostly hype for Power BI teams. Today, it deserves your undivided attention. For Power BI pros, there is now a real opportunity to reduce repetitive development work, accelerate delivery, and help developers do more, but only when strong DataOps practices are in place to make AI workflows effective. This session is a no-nonsense introduction to effective AI patterns for Power BI and Fabric development. Along the way, we will make sense of the growing pile of terminology, including skills, plugins, hooks, and MCP. You will see examples of how modern AI tooling can help with development tasks across Power BI and Fabric, along with the prerequisites, guardrails, and DataOps principles needed to use it responsibly. Whether you're burned out on AI hype or already using Copilot CLI daily, this session will show you the foundations that are finally making AI-assisted development genuinely useful.

Topic to be announced. **THANK YOU** *Franklin University* for hosting our meetup! To learn more about *Franklin University*, please visit their website: https://www.franklin.edu/ **DIRECTIONS** Franklin University Fisher Hall 300 E. Main St, Columbus, OH 43215 Map: https://maps.app.goo.gl/jxjBA2hUmS5qrvhq8 Parking is FREE! Please park in Lot C in front of Fisher Hall. See attached map. NOTE: Map the address only. When mapping with Google Maps it may use the Fisher Hall at OSU, which is NOT correct. **Want to sponsor the pizza and/or bar tab?** Please contact me if you would like to sponsor this meetup's pizza and/or bar tab: angelo@mandato.com

Join us for a practical, beginner-friendly guide to choosing the right large language model. We’ll compare major models like ChatGPT, Claude, Gemini, and Llama, talk about when to use hosted APIs versus local models, and break down the tradeoffs around cost, speed, quality, privacy, context windows, coding ability, and reliability. You’ll leave with a clearer mental model for picking an LLM based on your actual use case instead of hype, benchmarks, or brand names. No deep AI background required. LOGISTICS AND PARKING: The talk starts at 7:00 PM. The first half hour is reserved for everyone to get set up and mingle. Free pizza and drinks! The cheapest parking option is to find street parking, which will only cost you a few bucks. Otherwise, park in the nearby veteran's museum lot for $8. It's highly recommended you avoid the nearby $15 garage parking.

NFT's are here to stay folks! This is a group for like minded people interested in understanding, leveraging, using, creating for, profiting from, trading too i suppose, NFT's.. everything around them, complexity, fear and exploits, best practices and more. **PLUS** This group will talk AI ART tools, techniques, artists, video, audio, prototypes and more in the AI assisted production space- ART specifically, but we can get into any aspect of some of the cooler things happening in AI in general.