RAIL Hosts Expert-Led Training on AI Productionization and Commercialization
The Responsible AI Lab (RAIL) organised an educative training session on AI productionization and commercialisation on April 8, 2025, led by Mr. Darlington Akogo (CEO of MinoHealth AI Labs) and Mr. Jeremiah Ayensu (Senior AI Engineer at MinoHealth AI Labs). The session equipped about sixty (60) participants with practical knowledge on deploying AI systems to solve real-world problems and strategies for successfully commercialising AI innovations.
Mr. Jeremiah Ayensu guided attendees through the technical aspects of AI deployment, outlining a structured application developmentenvironment consisting of three key phases: the development environment, staging environment and production environment.
He highlighted unique challengesin AI production, including model explainability requirements, performance variability, and resource intensity.
He advised that if AI is not required to build a solution, it shouldn’t be used, especially when predictive features are unnecessary.
He mentioned that building an AI system is all about data, not any data but relevant data. “In the real world, data is very important, not only in quantity and quality but preprocessing the data,” he added.
He highlighted the end-to-end AI production pipeline, which spans model development, deployment, monitoring, and iteration. He mentioned setup options, GPU infrastructure and optimising techniques as key infrastructures for AI production. He said AI systems typically combine predictive AI components with traditional software and encourage continuous learning in the rapidly evolving AI landscape.
A case study on Moremi AI, MinoHealth’s multi-modal large language model for healthcare, illustrated these principles. Moremi AI assists in medical diagnosis, report generation, and drug design, showcasing how AI can transform industries.
Mr. Darlington Akogo explored AI commercialisation strategies, outlining viable business and revenue models.
He said some revenue models are subscription pricing, usage-based pricing, licensing and outcome-based pricing. He iterated that to build trust and loyalty in customer engagement, there should be personised and tailor-made interactions, proactiveness where issues are articulated, and feedback.
He identified challenges like AI ethics and regulatory compliances where bias, fairness and legal frameworks are addressed. “Scalability and infrastructure costs and competition and differentiation should also be considered in commercialising AI,” he stated
The training provided a holistic view of AI’s journey—from technical deployment to sustainable business models- and gave actionable insights on how to turn AI innovations into impactful, commercially viable solutions.
