Solving Data Sufficiency Issues in Machine Learning Projects (Part 3)
The AutoML Conundrum: Introducing the Semantic Intelligence Engine Welcome to Part 3 of this interesting series on how we can employ semantic techniques to speed up machine learning projects and at the same time ensure better outcomes because the data that we started with in the first place is closest to the business requirement. You can find Part 1 and Part 2 here and here respectively. In this part, let us examine the question: How can we Solve Data Sufficiency Issues in AutoML Pipelines. We plan to introduce an hypothetical component called the "Semantic Intelligence Engine". To begin with what is Data Sufficiency, when we examine in the context of AutoML? Mainly two points: Having sufficient data to proceed with an ML task Having enough knowledge to fill in the gaps in data if required Thus, having an ecosystem to enable cross context (or even cross domain) application of data assets in the long run In this post we present the idea of a “Semantic In
