Starnley Odiwuor

Starnley Odiwuor is a Master's student at Maseno University and a Research Associate at MCAAI, where he leads the centre's MLOps engineering efforts — ensuring that AI models developed across MCAAI's research programmes are deployed, monitored, and maintained as stable, scalable systems that serve real users reliably in the field.

The gap between a trained model and a production deployment is one of the most challenging engineering problems in applied AI, particularly in African research environments where cloud infrastructure costs, connectivity constraints, and device heterogeneity add significant complexity. Starnley designs and maintains the CI/CD pipelines, model versioning systems, experiment tracking infrastructure, and performance monitoring dashboards that form the backbone of MCAAI's production AI environment.

His work has been instrumental in enabling MCAAI to move from research prototype to live deployment across multiple projects. The AI Farm Assistant's USSD and web deployment, the DhoNam dataset release pipeline, and the AI4KSL avatar rendering backend all depend on the infrastructure Starnley has built and continues to maintain.

Starnley is also developing expertise in edge deployment — optimising AI models for low power devices and intermittent connectivity environments, which is essential for MCAAI's mission of reaching communities in rural Western Kenya. He works in close collaboration with Biatus Maina on the centre's broader machine learning engineering infrastructure.