Building Blocks of an AI Framework for an Enterprise

Abstract

Speaking broadly, an AI framework consists of six main layers starting with Data Integration layer. All AI applications need some kind of integration with the input data sources from enterprise applications. So, it is essential that AI framework has the capability to integrate with different data sources for seamless data exchange. This study is an overview of the building blocks of an AI Framework to deal as an analytic tool for the manager’s tussle with AI’s influence on their industries. The core of the framework is an “AI Ecosystem” where all AI, ML & NLP capabilities or “AI Assets” will reside and will have the option to pick and choose the best of the breed capabilities for building AI, ML applications. These AI capabilities can be versatile, like cognitive text processing, speech, computer vision, cognitive search, etc. Also, the framework should be able to connect to different hosting applications or channels to host AI applications or solutions. It is also advisable to have a Framework Management layer wherein features like setup & configuration, monitoring of different services, monitoring and reporting can be embedded. As security is one of the key elements of any framework, it should also be accounted for while designing a sustainable AI framework. Such a framework is going to provide flexibility and agility while building any AI solution. As AI is purely data-driven, this study intends to provide an insight into an enterprise-wise policy or data standard to designing and assembling an AI system.

Keywords: AI Framework, Data Integration Layer, AI Ecosystem, Computer Vision and Cognitive Search

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