For AI to Succeed, MLOps Needs a Bridge to DevOps

AI has been heralded as the new "brains" for software applications, a role long held by databases. Unfortunately, AI is not so easy for application developers and operations teams to adopt and absorb. Actually, incorporating machine-learning models (which power AI) in productivity-focused applications -- to make them smarter -- is overly difficult and complex. Moreover, ML models depend on specific combinations of hardware and software infrastructure. Without the right infrastructure, the models either cannot perform well enough to be viable or, in some cases, become prohibitively costly.