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.
This work is sponsored by the EU ISFP ProTECT grant on Public Resilience using TEchnology to Counter Terrorism.
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van den Heuvel, WJ., Tamburri, D.A. (2020). Model-Driven ML-Ops for Intelligent Enterprise Applications: Vision, Approaches and Challenges. In: Shishkov, B. (eds) Business Modeling and Software Design. BMSD 2020. Lecture Notes in Business Information Processing, vol 391. Springer, Cham. https://doi.org/10.1007/978-3-030-52306-0_11
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