Related papers: A Comprehensive Multi-Vocal Empirical Study of ML …
Multilingual Large Language Models (MLLMs) represent a pivotal advancement in democratizing artificial intelligence across linguistic boundaries. While theoretical foundations are well-established, practical implementation guidelines remain…
Cloud applications today deliver an increasingly larger portion of the Information and Communication Technology (ICT) services. To address the scale, growth, and reliability of cloud applications, self-aware management and scheduling are…
To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and available resources have significantly advanced LLMs, allowing their…
With the ever-growing data and the need for developing powerful machine learning models, data owners increasingly depend on various untrusted platforms (e.g., public clouds, edges, and machine learning service providers) for scalable…
Cloud applications are increasingly shifting from large monolithic services, to complex graphs of loosely-coupled microservices. Despite their advantages, microservices also introduce cascading QoS violations in cloud applications, which…
Migration legacy systems to cloud platforms is a knowledge intensive process. There is an ever increasing body of knowledge reporting empirical scenarios of successful and problematic cloud migration. Reusing this body of knowledge,…
Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions assessing…
Large language models (LLMs) are widely applied in chatbots, code generators, and search engines. Workload such as chain-of-throught, complex reasoning, agent services significantly increase the inference cost by invoke the model…
In an era defined by the explosive growth of data and rapid technological advancements, Multimodal Large Language Models (MLLMs) stand at the forefront of artificial intelligence (AI) systems. Designed to seamlessly integrate diverse data…
Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, based on well over a dozen case studies, we have learned that deploying industry-strength, production quality ML models in systems…
Large language models (LLMs) are becoming increasingly widespread. Organizations that want to use AI for productivity now face an important decision. They can subscribe to commercial LLM services or deploy models on their own…
The rapid rise of Large Language Models (LLMs) has revolutionized various artificial intelligence (AI) applications, from natural language processing to code generation. However, the computational demands of these models, particularly in…
Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional…
While machine learning (ML) technology affects diverse stakeholders, there is no one-size-fits-all metric to evaluate the quality of outputs, including performance and fairness. Using predetermined metrics without soliciting stakeholder…
The use of machine learning (ML) methods for prediction and forecasting has become widespread across the quantitative sciences. However, there are many known methodological pitfalls, including data leakage, in ML-based science. In this…
The momentum gained by microservices and cloud-native software architecture pushed nowadays enterprise IT towards multi-service applications. The proliferation of services and service interactions within applications, often consisting of…
The use of machine learning or artificial intelligence (ML/AI) holds substantial potential toward improving many functions and needs of the public sector. In practice however, integrating ML/AI components into public sector applications is…
Through past experiences deploying what we call usable ML (one step beyond explainable ML, including both explanations and other augmenting information) to real-world domains, we have learned three key lessons. First, many organizations are…
Machine learning (ML) pervades an increasing number of academic disciplines and industries. Its impact is profound, and several fields have been fundamentally altered by it, autonomy and computer vision for example; reliability engineering…
AI/ML-based intrusion detection systems (IDSs) and misbehavior detection systems (MDSs) have shown great potential in identifying anomalies in the network traffic of networked autonomous systems. Despite the vast research efforts, practical…