An open extensible AI platform implementing four use cases for the enterprise

Description

Robotic process automation and intelligent process automation address the challenges in automating mechanical processes involved in integrating disparate systems with unattended workflows. The building blocks for such platforms require a robust AI platform with APIs and frameworks for machine learning algorithms and deep learning algorithms. Those APIs need a clean abstraction for the underlying ML/DL layer unless an enterprise is comfortable locking itself into one from an established cloud vendor. Open source solutions like PredictionIO provide a nice abstraction layer to plug and play many AI/ML frameworks that can run on CPUs, GPUs, and custom hardware like TPUs and FPGAs. With an Agile workflow and process automation solution, you can orchestrate the invocation of those APIs and build a progressive web application with open frameworks that provides a rich and effective user experience.

Murali Kaundinya outlines an InnerSource model to curate and operationalize machine learning and deep learning algorithms with a common workflow and engaging user experience. Focusing on patterns and practices, Murali then shares lessons learned implementing four enterprise scale use cases: optical character recognition, release engineering, virtual customer assistants, and data unification.