Moving an AI project from ideation to realization is a vicious loop, and there is only one way to resolve it – don’t let the loop begin! That is true because data deserves expert handling at all levels. Starting with extracting it from different sources to cleaning, analyzing, and populating it, machine learning systems are prone to latencies if the underlying architecture lacks an operational approach to ML – known as MLOps.