A software engineering perspective on engineering machine learning systems: State of the art and challenges

G Giray - Journal of Systems and Software, 2021 - Elsevier
Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

An artificial intelligence life cycle: From conception to production

D De Silva, D Alahakoon - Patterns, 2022 - cell.com
This paper presents the" CDAC AI life cycle," a comprehensive life cycle for the design,
development, and deployment of artificial intelligence (AI) systems and solutions. It …

[HTML][HTML] AI system architecture design methodology based on IMO (Input-AI Model-Output) structure for successful AI adoption in organizations

S Park, J yoon Lee, J Lee - Data & Knowledge Engineering, 2024 - Elsevier
With the advancement of AI technology, the successful AI adoption in organizations has
become a top priority in modern society. However, many organizations still struggle to …

Advancing Translation Quality Assessment: Integrating AI Models for Real-time Feedback

SY Mohammed, M Aljanabi - EDRAAK, 2024 - peninsula-press.ae
With the use of AI models for real-time reception a feedback, this review attempts to show
how the assessment of the quality of translation is changing. The investigation covers …

A Framework to Model ML Engineering Processes

S Morales, R Clarisó, J Cabot - ar** contemporary
flexible and evolvable software systems. Therefore, microservices enable scalability, agility …

Towards an Evaluation Model to Quantitively Measure Toolchain Interoperability: A Case Study of Digital Hardware Design

JM Alvarez-Rodríguez, R Mendieta, E Cibrián… - Available at SSRN … - papers.ssrn.com
The development of complex systems is becoming more challenging than ever. The need of
continuously delivering new products in a timely and cost-effective manner is a cornerstone …