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MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of…

Machine learning models are widely recognized for their strong performance in forecasting. To keep that performance in streaming data settings, they have to be monitored and frequently re-trained. This can be done with machine learning…

Econometrics · Economics 2025-04-24 Yu Jeffrey Hu , Jeroen Rombouts , Ines Wilms

In the last few decades, Machine Learning (ML) has achieved significant success across domains ranging from healthcare, sustainability, and the social sciences, to criminal justice and finance. But its deployment in increasingly…

Machine Learning · Computer Science 2025-09-03 Nathan Justin , Qingshi Sun , Andrés Gómez , Phebe Vayanos

Conventional machine learning studies generally assume close-environment scenarios where important factors of the learning process hold invariant. With the great success of machine learning, nowadays, more and more practical tasks,…

Machine Learning · Computer Science 2022-08-10 Zhi-Hua Zhou

The increased complexity and dynamism of present and future Multi-Agent Systems (MAS) enforce the need for considering both of their static (design-time) and the dynamic (run-time) aspects. A type of balance between the two aspects can…

Multiagent Systems · Computer Science 2015-11-24 Hosny Abbas , Samir Shaheen

Machine learning (ML) models are increasingly being used in application domains that often involve working together with human experts. In this context, it can be advantageous to defer certain instances to a single human expert when they…

Artificial Intelligence · Computer Science 2022-06-17 Patrick Hemmer , Sebastian Schellhammer , Michael Vössing , Johannes Jakubik , Gerhard Satzger

MLOps has emerged as a key solution to address many socio-technical challenges of bringing ML models to production, such as integrating ML models with non-ML software, continuous monitoring, maintenance, and retraining of deployed models.…

Software Engineering · Computer Science 2025-04-17 Beyza Eken , Samodha Pallewatta , Nguyen Khoi Tran , Ayse Tosun , Muhammad Ali Babar

Large language models (LLMs) have gained significant interest in industry due to their impressive capabilities across a wide range of tasks. However, the widespread adoption of LLMs presents several challenges, such as integration into…

Artificial Intelligence · Computer Science 2025-04-14 Eser Kandogan , Nikita Bhutani , Dan Zhang , Rafael Li Chen , Sairam Gurajada , Estevam Hruschka

Large language models (LLM) are advanced AI systems trained on extensive textual data, leveraging deep learning techniques to understand and generate human-like language. Today's LLMs with billions of parameters are so huge that hardly any…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-10-14 Sheikh Azizul Hakim , Saem Hasan

It is likely that AI systems driven by pre-trained language models (PLMs) will increasingly be used to assist humans in high-stakes interactions with other agents, such as negotiation or conflict resolution. Consistent with the goals of…

Computation and Language · Computer Science 2023-03-24 Alan Chan , Maxime Riché , Jesse Clifton

Applications of machine learning (ML) to high-stakes policy settings -- such as education, criminal justice, healthcare, and social service delivery -- have grown rapidly in recent years, sparking important conversations about how to ensure…

Machine Learning · Computer Science 2021-05-14 Hemank Lamba , Kit T. Rodolfa , Rayid Ghani

The integration of Large Language Models (LLMs) into Security Operations Centres (SOCs) presents a transformative, yet still evolving, opportunity to reduce analyst workload through human-AI collaboration. However, their real-world…

Cryptography and Security · Computer Science 2025-09-22 Ronal Singh , Shahroz Tariq , Fatemeh Jalalvand , Mohan Baruwal Chhetri , Surya Nepal , Cecile Paris , Martin Lochner

We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are presented in the form of antipatterns. Just as design…

As AI is increasingly being adopted into application solutions, the challenge of supporting interaction with humans is becoming more apparent. Partly this is to support integrated working styles, in which humans and intelligent systems…

Artificial Intelligence · Computer Science 2017-10-02 Maria Fox , Derek Long , Daniele Magazzeni

Large language models have gained widespread popularity for their ability to process natural language inputs and generate insights derived from their training data, nearing the qualities of true artificial intelligence. This advancement has…

Software Engineering · Computer Science 2024-11-25 Narcisa Guran , Florian Knauf , Man Ngo , Stefan Petrescu , Jan S. Rellermeyer

Increasing availability of machine learning (ML) frameworks and tools, as well as their promise to improve solutions to data-driven decision problems, has resulted in popularity of using ML techniques in software systems. However,…

Software Engineering · Computer Science 2021-03-29 Grace A. Lewis , Stephany Bellomo , Ipek Ozkaya

Machine Learning (ML) techniques have begun to dominate data analytics applications and services. Recommendation systems are a key component of online service providers. The financial industry has adopted ML to harness large volumes of data…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-03-29 Richard Mortier , Hamed Haddadi , Sandra Servia , Liang Wang

Containerization is a lightweight application virtualization technology, providing high environmental consistency, operating system distribution portability, and resource isolation. Existing mainstream cloud service providers have…

Machine Learning · Computer Science 2021-08-23 Zhiheng Zhong , Minxian Xu , Maria Alejandra Rodriguez , Chengzhong Xu , Rajkumar Buyya

Resource-aware machine learning has been a trending topic in recent years, focusing on making ML computational aspects more exploitable by the edge devices in the Internet of Things. This paper attempts to review a conceptually and…

Machine Learning · Computer Science 2021-11-02 Vahid Mohammadi Safarzadeh , Hamed Ghasr Loghmani

The implementation of AI-based applications in complex environments often requires the collaboration of several devices spanning from edge to cloud. Identifying the required devices and configuring them to collaborate is a challenge…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-11-18 Mario Scrocca , Marco Grassi , Alessio Carenini , Jean-Paul Calbimonte , Darko Anicic , Irene Celino