Abstract
Machine Learning (ML) applications are growing in an unprecedented scale. The development of easy-to-use machine-learning application frameworks has enabled the development of advanced artificial intelligence (AI) applications with only a few lines of self-explanatory code. As a result, ML-based AI is becoming approachable by mainstream developers and small businesses. However, the deployment of ML algorithms for remote high throughput ML task execution, involving complex data-processing pipelines can still be challenging, especially with respect to production ML use cases. To cope with this issue, in this paper we propose a novel system architecture that enables Algorithm-agnostic, Scalable ML (ASML) task execution for high throughput applications. It aims to provide an answer to the research question of how to design and implement an abstraction framework, suitable for the deployment of end-to-end ML pipelines in a generic and standard way. The proposed ASML architecture manages horizontal scaling, task scheduling, reporting, monitoring and execution of multi-client ML tasks using modular, extensible components that abstract the execution details of the underlying algorithms. Experiments in the context of obstacle detection and recognition, as well as in the context of abnormality detection in medical image streams, demonstrate its capacity for parallel, mission critical, task execution.
Highlights
Deep learning growth has triggered the appearance of frameworks for easy development of Machine Learning (ML)-enabled applications
WORK There has been work towards scalable system architectures and application frameworks that aim to provide scalable task execution. When it comes to ML, the deployment of such systems tends to be complicated and usually coupled to specific domains and use cases
The lack of an abstraction framework for the whole ML pipeline and need for a generic and standard deployment approach has been highlighted in the recent literature [23], [24]
Summary
Deep learning growth has triggered the appearance of frameworks for easy development of ML-enabled applications. It is possible to deploy pre-trained models such as [3] and [4], as long as they are implemented in a supported framework The flexibility of such services is limited, as while it is relatively easy to get started, it is difficult to efficiently incorporate ML models based on novel components, such as the fuzzy pooling layer proposed in [5], or complex ML-based data-processing pipelines, such as pipelines that include image preprocessing, integration of multiple heterogeneous ML algorithms with bidirectional data communication. This paper addresses the problem of remote high throughput ML task execution involving complex data-processing pipelines It aims to cope with well-recognized challenges [23], [24] that include the deployment of ML applications in a generic and standard way through a framework that provides the necessary level of abstraction. The last section along with discusses for future work and perspectives
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