CFP closed at  December 16, 2023 15:18 UTC


The RVA Tech Data/AI Summit is a one-day conference dedicated to all things data engineering and data science. Richmond and the greater central Virginia area have an amazing, vibrant data science and engineering community and this conference is our chance to celebrate the practitioners in our community. This event is held at one of the city’s crown event spaces, the Virginia Museum of Science’s Dewey Gottwald Center. 2024 will be the sixth year for Data Summit. Our past versions were great events with sell-out crowds of over 400 attendees. We are anticipating plenary keynote sessions and then multiple concurrent talks in separate tracks, at roughly 45 minutes each with ~5 planned session times throughout the day. We would like to have Q&A time, please incorporate it into the 45-minute session time allotted. We are interested in any and all topics surrounding the vast Data Science, AI, and Data Engineering domains. Our audience will be about 400 people with a broad range of data science and engineering exposure ranging from students to deep practitioners to leadership.


March 28, 2024 - All Day Contact : Todd Dube for more information


Science Museum of Virginia - Dewey Gottwald Center 2301 West Leigh Street Richmond, VA 23220


The RVA Data Summit is run by a diverse team of volunteer committee members in service of the Richmond Technology Council. Volunteers and the council share a common goal of bringing the data community together.


Everyone is encouraged to submit a proposal. Our primary goal is to have excellent sessions that inform and engage our attendees. Thus, the speaker selection committee will be favoring speakers who have a record of delivering excellent talks, but we also understand that everyone has to start somewhere. If you are a first-time speaker, please make sure your proposal is compelling and communicates why you are the right person to deliver it. We are hoping to have a speaker pool that is representative of our local developer population and showcases some regional talent, but we welcome submissions from anyone anywhere. If you have an idea for a session, please submit it!

CFP Description

This is and has always been a practitioner-focused event. We want to provide as much learning and education as possible. The focus on this is making learning accessible and helpful. This event is NOT about product showcases or hand-wavy leadership talks. Potential talks ought to be applicable to building models or managing teams of real data scientists and engineers with real-world problems. All talks submitted will be viewed from that perspective.

Track 1: AI-Driven Workplaces and Customer Experiences

Description: This track explores the transformative impact of AI on where we work and how we interact with customers. It will highlight real-world success stories and future applications of driving value with AI. The use of GANs or LLM models in modern applications is particularly of interest.

Hands-on workshops that showcase the capabilities are encouraged. ##Target Audience: Talks should be accessible for leadership, practitioners and students

Example talks could be:

“Reimagining Customer Support: How AI-Powered Chatbots Enhance Customer Experience” “AI-Driven Employee Productivity: Leveraging Natural Language Processing for Knowledge Management” “Keeping your Data Secure in a World of AI”

Track 2: Building and Deploying ML Models

Description: Driving value for businesses requires robust, well-managed models that are deployed successfully. This track dives into the practical aspects of developing and deploying machine learning models, encompassing hands-on workshops, tool demonstrations, and insights from experienced data practitioners.

Target Audience: All talks should target practitioners, but also looking for some talks to be student-friendly

Example talks could be:

“Hands-On: Building Your First Machine Learning Model with Python and Scikit-learn” “MLOps: Streamlining the ML Model Deployment Lifecycle” “Best Practices for Using Automated Modeling in Sagemaker”

Track 3: Data/AI Ethics and Social Impact

Description: This track focuses on harnessing the power of Data + AI for social good, addressing critical global challenges, and ethical considerations in the data and AI spaces. The applications could be in engineering, analysis, visualization, or modeling. Talks specifically about data in the local region would be of great interest.

Target Audience: All talks should target practitioners and leadership friendly, preferably with some content for each.

Example talks could be: “AI in Healthcare: Revolutionizing Diagnostics and Patient Care” “AI for Energy Conservation: Preserving Biodiversity through Data-Driven Solutions” “Ensuring Ethical Data Collection: A Framework for Collection and Storage of Customer Data”

Track 4: Modern Data Engineering

Description: This track focuses on modern data engineering tools and solutions that generate actionable data, with an interest in talks around data quality and governance practices.

Target Audience: All talks should target practitioners, but also looking for some talks to be student-friendly

Example talks could be:

“Building Scalable Data Pipelines with Apache Beam and Spark: Lessons Learned” “Data Governance in the Age of Big Data: Ensuring Data Quality and Compliance” “Actionable Insights: Leveraging Modern Data Engineering Tools for Business Impact”

Track 5: Leading in the Age of AI: Strategies and Insights

Description: Delve into AI’s influence on organizational leadership and the strategies needed to thrive in the AI era. This track explores how to pick good AI projects, navigate through AI hype to real value, and general considerations of AI in leadership.

Target Audience: Leadership and Aspiring Practitioners

Example talks could be:

“AI Leadership: Navigating Cultural Transformation for AI Adoption” “AI Talent Acquisition and Upskilling: Fostering a Data-Driven Workforce” “From Idea to Implementation: Key Steps for Starting an AI Project Successfully”

Track 6: Building Careers in Data

Description: This track is tailored for individuals looking to build or transition their careers into the field of data. It provides guidance, insights, and practical advice on making a successful career into data, including learning paths, success stories, skill development, and networking strategies.

Target Audience: Professionals from diverse backgrounds and/or students interested in starting a career in data

Example talks could be: “From Non-Technical to Data Scientist: Navigating Your Career Transition” “Building a Strong Foundation: Essential Skills for a Career in Data Science” (edited)