Building Robust AI Systems: Beyond the Algorithm
Many envision that creating AI solutions is primarily about the AI model itself, but the reality, according to software engineer Ayodeji Erinfolami, is far more complex. Erinfolami, a seasoned professional with experience as a co-founder of an AI-driven SaaS company and a former senior engineer at numerous tech firms, emphasizes that getting the AI to function is only a fraction of the overall effort. The true challenge lies in constructing reliable and scalable systems around these models.
Tackling AI’s Unpredictability and Costs
Erinfolami highlights that a significant portion of the work, around 80%, is dedicated to managing AI’s inherent unpredictability. This includes addressing issues such as fluctuating response times, the potential for escalating costs, and establishing contingency plans for instances where the AI models fail to deliver the desired results. In his experience, understanding the real token cost is also critical. During development, it quickly became apparent that expenses could easily spiral out of control.
Ultimately, building successful AI systems requires a comprehensive approach that extends far beyond just the AI model. It demands careful consideration of infrastructure, cost management, and robust error handling to ensure reliable and sustainable performance.
Keywords
Related Keywords: building artificial intelligence, AI development, AI programming, AI implementation, machine learning development, artificial intelligence engineering, AI architecture, AI infrastructure, training AI models, custom AI solutions
