22-25 April 2026

Gift Ojeabulu

Gift Ojeabulu is a data scientist, AI/ML practitioner, and community builder with over six years of experience at the intersection of artificial intelligence, software engineering, and developer advocacy. He has led and scaled global AI communities, including growing Iterative.ai’s community to over 30,000 data, ML, and AI professionals worldwide. Gift has curated hundreds of technical content pieces annually and has worked with AI startups such as Deci AI and DagsHub to shape developer relations and content strategies for highly technical audiences. He is a four-time AWS Community Builder in Machine Learning and AI and serves as a board advisor to DevNetwork (USA) in artificial intelligence and developer advocacy. As the co-founder of Data Community Africa (D.C.A), the largest Black data and AI community on the continent, Gift has led initiatives that support education, open-source collaboration, and professional growth, including the African Data Community Newsletter reaching over 2,500 subscribers across 80 countries. He has contributed to major ecosystem efforts such as DatafestAfrica and leads the Lagos MLOps community, where he focuses on practical MLOps, large language models, and open-source AI development. Through his work, Gift actively advances Africa’s data and AI ecosystem by connecting local talent to global opportunities and fostering sustainable innovation.

Sessions for 2026

AI in Action: Developing Smarter, Faster Data Platforms with LLMs and Copilot
 

Moderating a panel exploring practical AI adoption in data platforms, including LLMs, Copilot, and AI agents, with real-world lessons, trade-offs, and productivity tips for data professionals.

Code Your Own Sports Analytics Dashboard
 

Build an interactive sports analytics dashboard with Python, pandas, and Streamlit while learning practical data science workflows and visualization techniques.

Data Validation in Production ML: Preventing Silent Failures with Pandera, GE, DBT and Deepchecks
 

Learn to catch silent ML failures in production using Pandera, Great Expectations, Deepchecks, and Evidently AI with practical code patterns and workflow strategies.

Speed vs. Scale: DuckDB, Polars, Pandas, and PySpark in Practice
 

Cut through Python data tool hype with practical benchmarks and decision frameworks for DuckDB, Polars, Pandas, and PySpark to pick the right tool for speed vs. scale.