Even with the onset of Artificial Intelligence (AI)’s recent advancements and perhaps new or at least reiterations of all that it promises, some organizations continue to wait or “put off” any serious investigation of the technology.
In an earlier post (IBM Planning Analytics Data Modelling with Context) I stated that when modeling data as part of a planning analytics solution design, context clues should be developed, through a process referred to as profiling and then “built in” to the data.
In the past, data to be modeled came from a single source and was provided in the same format, typically transactions from a general ledger system. In today’s data driven world, project data can come from a variety of places which, potentially, can influence the data’s possible meaning or value, effect how you model and use it and ultimately, whether it will provide insights the business can in fact leverage.
So, what is model serviceability?
Often you hear about performing an application design review on a IBM Planning Analytics model where both coding and implementation “styles” are compared against “industry proven” practices. During the process, naming conventions, dimensionalities, rule-vs-process strategies, (just to name a few items) are studied and assessed.
The IBM Watson Visual Recognition Service is one of the many services available on the IBM Cloud platform designed to accelerate and automate the AI Lifecycle by simplifying the most complicated, time-consuming steps within a VR project.