This paper presents a Large Language Model (LLM) based conversational agent system designed to enhance human-machine collaboration in Machine Learning Operations (MLOps). We introduce the Swarm Agent, an extensible architecture that integrates specialized agents to create and manage ML workflows through natural language interactions. The system leverages a hierarchical, modular design incorporating a KubeFlow Pipelines (KFP) Agent for ML pipeline orchestration, a MinIO Agent for data management, and a Retrieval-Augmented Generation (RAG) Agent for domain-specific knowledge integration. Through iterative reasoning loops and context-aware processing, the system enables users with varying technical backgrounds to discover, execute, and monitor ML pipelines; manage datasets and artifacts; and access relevant documentation, all via intuitive conversational interfaces. Our approach addresses the accessibility gap in complex MLOps platforms like Kubeflow, making advanced ML tools broadly accessible while maintaining the flexibility to extend to other platforms. The paper describes the architecture, implementation details, and demonstrates how this conversational MLOps assistant reduces complexity and lowers barriers to entry for users across diverse technical skill levels.

Nowadays, machine learning (ML) teams have multiple concurrent ML workflows for different applications. Each workflow typically involves many experiments, iterations, and collaborative activities and commonly takes months and sometimes years from initial data wrangling to model deployment. Organizationally, there is a large amount of intermediate data to be stored, processed, and maintained. \emph{Data virtualization} becomes a critical technology in an infrastructure to serve ML workflows. In this paper, we present the design and implementation of a data virtualization service, focusing on its service architecture and service operations. The infrastructure currently supports six ML applications, each with more than one ML workflow. The data virtualization service allows the number of applications and workflows to grow in the coming years.

--Reproducibility remains a central challenge in machine learning (ML), especially in collaborative eScience projects where teams iterate over data, features, and models. Current ML workflows are often dynamic yet fragmented, relying on informal data sharing, ad hoc scripts, and loosely connected tools. This fragmentation impedes transparency, reproducibility, and the adaptability of experiments over time. This paper introduces a data-centric framework for lifecycle-aware reproducibility, centered around six structured artifacts: Dataset, Feature, Workflow, Execution, Asset, and Controlled V ocabulary. These artifacts formalize the relationships between data, code, and decisions, enabling ML experiments to be versioned, interpretable, and traceable over time. The approach is demonstrated through a clinical ML use case of glaucoma detection, illustrating how the system supports iterative exploration, improves reproducibility, and preserves the provenance of collaborative decisions across the ML lifecycle. As machine learning (ML) becomes increasingly central to scientific discovery, concerns about correctness and reproducibility have grown [1]. In eScience, ML development is typically a collaborative and iterative process involving domain experts, data engineers, and ML researchers. These teams refine models based on evolving hypotheses and new data, creating feedback loops across data curation, feature engineering, modeling, and evaluation [2]. This dynamic process frequently introduces data cascades, where early curation errors propagate downstream, compounding over time [3]. In practice, ML workflows remain fragmented: datasets are shared informally, experiments span personal and cloud environments, and data, code, and configurations are often loosely coupled [4]. While MLOps and data management tools address parts of this problem, such as code versioning, pipeline orchestration, or environment encapsulation, they often overlook the full scientific lifecycle and the socio-technical realities of collaborative ML projects [5]. In prior work, we introduced Deriva-ML [6], a socio-technical platform that extends the FAIR principles (Findable, Accessible, Interoperable, Reusable) [7] across the ML developmental lifecycle.

The reference to assumptions in how practitioners use or interact with machine learning (ML) systems is ubiquitous in HCI and responsible ML discourse. However, what remains unclear from prior works is the conceptualization of assumptions and how practitioners identify and handle assumptions throughout their workflows. This leads to confusion about what assumptions are and what needs to be done with them. We use the concept of an argument from Informal Logic, a branch of Philosophy, to offer a new perspective to understand and explicate the confusions surrounding assumptions. Through semi-structured interviews with 22 ML practitioners, we find what contributes most to these confusions is how independently assumptions are constructed, how reactively and reflectively they are handled, and how nebulously they are recorded. Our study brings the peripheral discussion of assumptions in ML to the center and presents recommendations for practitioners to better think about and work with assumptions.

As model parameter sizes reach the billion-level range and their training consumes zettaFLOPs of computation, components reuse and collaborative development are become increasingly prevalent in the Machine Learning (ML) community. These components, including models, software, and datasets, may originate from various sources and be published under different licenses, which govern the use and distribution of licensed works and their derivatives. However, commonly chosen licenses, such as GPL and Apache, are software-specific and are not clearly defined or bounded in the context of model publishing. Meanwhile, the reused components may also have free-content licenses and model licenses, which pose a potential risk of license noncompliance and rights infringement within the model production workflow. In this paper, we propose addressing the above challenges along two lines: 1) For license analysis, we have developed a new vocabulary for ML workflow management and encoded license rules to enable ontological reasoning for analyzing rights granting and compliance issues. 2) For standardized model publishing, we have drafted a set of model licenses that provide flexible options to meet the diverse needs of model publishing. Our analysis tool is built on Turtle language and Notation3 reasoning engine, envisioned as a first step toward Linked Open Model Production Data. We have also encoded our proposed model licenses into rules and demonstrated the effects of GPL and other commonly used licenses in model publishing, along with the flexibility advantages of our licenses, through comparisons and experiments.

In developing machine learning (ML) models for text classification, one common challenge is that the collected data is often not ideally distributed, especially when new classes are introduced in response to changes of data and tasks. In this paper, we present a solution for using visual analytics (VA) to guide the generation of synthetic data using large language models. As VA enables model developers to identify data-related deficiency, data synthesis can be targeted to address such deficiency. We discuss different types of data deficiency, describe different VA techniques for supporting their identification, and demonstrate the effectiveness of targeted data synthesis in improving model accuracy. In addition, we present a software tool, iGAiVA, which maps four groups of ML tasks into four VA views, integrating generative AI and VA into an ML workflow for developing and improving text classification models.

Background: As Machine Learning (ML) advances rapidly in many fields, it is being adopted by academics and businesses alike. However, ML has a number of different challenges in terms of maintenance not found in traditional software projects. Identifying what causes these maintainability challenges can help mitigate them early and continue delivering value in the long run without degrading ML performance. Aim: This study aims to identify and synthesise the maintainability challenges in different stages of the ML workflow and understand how these stages are interdependent and impact each other's maintainability. Method: Using a systematic literature review, we screened more than 13000 papers, then selected and qualitatively analysed 56 of them. Results: (i) a catalogue of maintainability challenges in different stages of Data Engineering, Model Engineering workflows and the current challenges when building ML systems are discussed; (ii) a map of 13 maintainability challenges to different interdependent stages of ML that impact the overall workflow; (iii) Provided insights to developers of ML tools and researchers. Conclusions: In this study, practitioners and organisations will learn about maintainability challenges and their impact at different stages of ML workflow. This will enable them to avoid pitfalls and help to build a maintainable ML system. The implications and challenges will also serve as a basis for future research to strengthen our understanding of the ML system's maintainability.

Machine learning methods are progressively gaining acceptance in the electron microscopy community for de-noising, semantic segmentation, and dimensionality reduction of data post-acquisition. The introduction of the APIs by major instrument manufacturers now allows the deployment of ML workflows in microscopes, not only for data analytics but also for real-time decision-making and feedback for microscope operation. However, the number of use cases for real-time ML remains remarkably small. Here, we discuss some considerations in designing ML-based active experiments and pose that the likely strategy for the next several years will be human-in-the-loop automated experiments (hAE). In this paradigm, the ML learning agent directly controls beam position and image and spectroscopy acquisition functions, and human operator monitors experiment progression in real-and feature space of the system and tunes the policies of the ML agent to steer the experiment towards specific objectives. One of the hallmarks of the meeting was the large number of presentations on machine learning (ML) in microscopy, ranging from denoising, unsupervised data analysis via variational autoencoders, and supervised learning applications for semantic segmentations and feature identification. Remarkably, by now most manufacturers offer or have plans to offer Python application programming interfaces (APIs), allowing the deployment of the codes on operational microscopes. From this perspective, the technical barriers for the broad implementation of automated microscopy in which ML algorithms analyze the data streaming from instrument detectors and make decisions based on this data are lower than ever.

Drag and drop tools have revolutionized the way we approach machine learning (ML) workflows. Gone are the days of manually coding every step of the process – now, with drag-and-drop interfaces, streamlining your ML pipeline has become more accessible and efficient than ever before.

Welcome back to my blog, where I share insights and tips on machine learning workflows using Sagemaker Pipelines. If you're new here, I recommend checking out my first post to learn more about this AWS fully managed machine learning service. In my second post, I discussed how parameterization can help you customize the workflow and make it more flexible and efficient. After using Sagemaker Pipelines extensively in real-life projects, I've gained a comprehensive understanding of the service. In this post, I'll summarize the key benefits of using Sagemaker Pipelines and the limitations you should consider before implementing it. This service is integrated with Sagemaker directly, so the user doesn't have to deal with other AWS services.