Model-driven ml-ops for intelligent enterprise applications: vision, approaches and challenges

Abstract

This paper explores a novel vision for the disciplined, repeatable, and transparent model-driven development and Machine-Learning operations (ML-Ops) of intelligent enterprise applications. The proposed framework treats model abstractions of AI/ML models (named AI/ML Blueprints) as first-class citizens and promotes end-to-end transparency and portability from raw data detection- to model verification, and, policy-driven model management. This framework is grounded on the intelligent Application Architecture (iA2) and entails a first attempt to incorporate requirements stemming from (more) intelligent enterprise applications into a logically-structured architecture. The logical separation is grounded on the need to enact MLOps and logically separate basic data manipulation requirements (data-processing layer), from more advanced functionality needed to instrument applications with intelligence (data intelligence layer), and continuous deployment, testing and monitoring of intelligent application (knowledge-driven application layer). Finally, the paper sets out exploring a foundational metamodel underpinning blueprint-model-driven MLOps for iA2 applications, and presents its main findings and open research agenda.