Tracking AI’s Footprints: Model Cards for the Edge

Author: Denis Avetisyan

As artificial intelligence moves closer to the data, ensuring accountability and understanding model behavior requires new approaches to metadata management.

A systematic pipeline integrates artificial intelligence and machine learning model development with deployment, incorporating crucial model card collection points to ensure transparency and facilitate rigorous evaluation of performance characteristics throughout the lifecycle.

This review examines dynamic model cards and communication protocols like REST and Model Context Protocol for tracking AI/ML provenance in edge computing environments, paving the way for more reliable and agentic AI systems.

While static evaluations offer limited insight into real-world AI/ML model behavior, this work, ‘AI/ML Model Cards in Edge AI Cyberinfrastructure: towards Agentic AI’, investigates dynamic model cards within edge computing environments to enhance accountability and track model provenance throughout their lifecycle. We present a comparative assessment of REST and the Model Context Protocol (MCP) for accessing model metadata, demonstrating quantitative performance tradeoffs alongside a qualitative evaluation of MCP’s utility in enabling active model sessions. Our findings suggest that MCP facilitates a more robust infrastructure for managing evolving model information. How can these dynamic model cards ultimately contribute to the development of truly agentic AI systems operating at the edge?

The Imperative of Transparency in Dynamic AI Systems

Contemporary artificial intelligence, despite demonstrating remarkable capabilities in controlled environments, frequently struggles with transparency and adaptability when deployed in the complexities of the real world. These models, often trained on static datasets, can exhibit unpredictable behavior when confronted with novel situations or shifting data distributions-a phenomenon known as ‘model drift’. This lack of inherent adaptability necessitates constant monitoring and potential retraining, creating significant operational overhead. Furthermore, the ‘black box’ nature of many advanced AI architectures-particularly deep neural networks-makes it difficult to understand why a model arrived at a specific decision, hindering trust and accountability. Consequently, organizations face challenges in reliably scaling AI solutions and ensuring responsible implementation, as unanticipated failures can have substantial consequences.

Current practices in assessing artificial intelligence often rely on evaluating models against fixed, static datasets, a methodology increasingly recognized as insufficient for real-world application. This approach fails to account for performance drift – the gradual degradation of accuracy as the model encounters data differing from its original training – and overlooks critical contextual nuances inherent in dynamic environments. A model performing flawlessly on a curated test set may exhibit significant errors when deployed in a constantly evolving situation, where shifts in input distributions or unforeseen edge cases can drastically alter its behavior. Consequently, the reliance on static evaluation provides an incomplete and potentially misleading picture of a model’s true reliability and generalizability, necessitating the development of methods capable of continuous monitoring and adaptation.

The reliable deployment of artificial intelligence necessitates a paradigm shift towards systems capable of perpetual self-assessment and refinement. Current approaches prioritize initial performance, yet real-world data is rarely static; models inevitably encounter novel inputs and shifting distributions, leading to performance degradation – a phenomenon known as ‘drift’. Consequently, a robust AI lifecycle demands continuous monitoring of model outputs, automated adaptation to changing conditions, and detailed documentation of all behavioral changes. This isn’t merely about tracking errors; it’s about creating an auditable record of why a model made a certain decision, enabling proactive intervention, responsible troubleshooting, and fostering trust in increasingly complex automated systems. Such dynamic systems are crucial for scaling AI applications safely and maximizing their long-term value, moving beyond one-time evaluations to sustained, intelligent operation.

The widespread implementation of artificial intelligence faces substantial hurdles without robust systems for continuous monitoring and adaptation. Deploying models at scale, across diverse and evolving real-world scenarios, introduces risks stemming from unpredictable performance drift and unforeseen contextual shifts. Without the ability to document model behavior and proactively address these changes, organizations risk inaccurate outputs, biased decisions, and erosion of public trust. This lack of accountability doesn’t simply impede technical progress; it actively hinders responsible innovation, creating a barrier to realizing the full potential of AI and fostering justifiable skepticism regarding its long-term benefits. Ultimately, the capacity to ensure reliable, adaptable, and transparent AI systems is not merely a technical challenge, but a prerequisite for its ethical and sustainable integration into society.

This diagram illustrates the complete lifecycle of an AI/ML model, from initial development through deployment and ongoing monitoring.

Patra: Establishing a Dynamic Record of Model Behavior

The Patra framework addresses limitations of static model cards by incorporating runtime behavior tracking. Traditional model cards document model characteristics at a fixed point in time; Patra extends this by continuously logging data generated during model inference. This includes recording the inputs provided to the model, the outputs it generates, and relevant environmental variables such as timestamps, hardware specifications, and software versions. By associating this runtime data directly with the corresponding model card, Patra creates a dynamic record of model performance and behavior over time, enabling continuous monitoring and analysis beyond initial evaluation metrics.

The Patra framework captures detailed runtime data during model inference, recording all inputs provided to the model, the corresponding outputs generated, and relevant environmental context. This context includes system metrics such as CPU usage, memory allocation, and timestamps, as well as software versions of dependencies. The collected data is structured to create a comprehensive audit trail, enabling reconstruction of specific inference events and facilitating detailed analysis of model behavior under varying conditions. This granular logging is crucial for debugging, performance monitoring, and identifying potential issues related to data drift or model staleness.

The Patra framework leverages Neo4j, a graph database, to manage the relationships between static model card information and dynamic runtime data. This implementation stores model cards, input data, predictions, and environmental variables as nodes and edges within the graph. The graph structure enables efficient querying and traversal to identify correlations between model behavior and specific input features, environmental conditions, or changes in data distributions. Queries can be constructed to retrieve all runtime instances associated with a particular model card, or conversely, all model cards that generated predictions for a specific input. This relational data storage facilitates detailed analysis of model performance, identification of potential failure modes, and tracking of data provenance throughout the inference lifecycle.

Patra facilitates the identification of model anomalies, performance regressions, and potential biases by establishing correlations between model outputs and associated contextual data. Captured runtime information, including input features, prediction results, and environmental variables, is linked to the originating model card. This linkage enables systematic analysis; for example, deviations from expected performance metrics can be traced back to specific input characteristics or environmental conditions. Furthermore, analysis of runtime data across different demographic groups or input distributions allows for the detection of disparate impact, indicating potential biases embedded within the model. The framework’s ability to store and query these relationships using graph databases, such as Neo4j, supports investigation into the root causes of identified issues and informs model refinement strategies.

The Patra Model Card consists of core entities detailing essential information about the model, its development, and intended use.

ICICLE: Deployment at the Edge with Continuous Observability

Edge computing, as implemented by the ICICLE AI Institute, addresses limitations inherent in traditional cloud-based AI deployments by processing data at or near the source of data generation. This proximity minimizes data transmission distances, directly reducing both latency – the delay between a request and a response – and bandwidth consumption. By avoiding the need to transfer large datasets to a centralized cloud server for processing, edge computing enables real-time or near real-time inference, critical for applications like autonomous systems and rapid anomaly detection. This distributed approach also enhances data privacy and security, as sensitive data remains localized and does not transit over networks.

The ICICLE infrastructure incorporates the Patra Model Card Framework to ensure comprehensive documentation and transparency of deployed AI models. This framework is integrated with a cyberinfrastructure leveraging the Tapis API for secure authentication and robust data management. Specifically, Tapis handles user credentials, access control, and data storage, while Patra provides a standardized method for detailing model characteristics, training data, performance metrics, and intended use cases. This combination facilitates model reproducibility, auditability, and responsible AI deployment by linking model metadata with the underlying computational resources and data pipelines managed by Tapis.

Model Images are containerized packages that encapsulate the trained AI model, its dependencies, and the runtime environment necessary for execution. This packaging facilitates portability and reproducibility across different edge computing deployments. The ICICLE infrastructure utilizes these Model Images to instantiate Inference Execution Instances, which are dedicated compute resources for running the models. As an example, the YOLO Object Detection Model is distributed and deployed as a Model Image, allowing for streamlined inference at the edge without requiring manual dependency management or configuration of the execution environment. This approach ensures consistent performance and simplifies the scaling of AI applications.

The ICICLE infrastructure provides ongoing observation of deployed model performance through continuous monitoring capabilities. Captured data regarding model inputs, outputs, and internal states is streamed and managed utilizing the CKN Streaming System. This integration facilitates the collection of telemetry data for analysis, enabling the tracking of model drift, identification of potential biases, and assessment of overall system health. The CKN Streaming System ensures reliable and scalable data capture, supporting both real-time monitoring and historical analysis of model behavior for auditing and improvement purposes.

ML Workbench facilitates machine learning inference directly on edge devices.

FAIR Data and the Foundation for Interoperable AI

The Patra framework champions data sharing and reuse by directly embedding itself within the established FAIR data principles. It achieves this through the utilization of RO-Crate, a standardized format for packaging research objects – encompassing data, code, and associated metadata – into a single, self-describing unit. This approach ensures that all necessary components for reproducibility and understanding are bundled together, promoting discoverability and facilitating automated processing. By leveraging RO-Crate, Patra not only adheres to the technical tenets of FAIR – Findable, Accessible, Interoperable, and Reusable – but also streamlines the process of sharing complex research outputs, fostering collaboration and accelerating scientific progress by creating a well-defined and easily distributed package of information.

The Patra framework significantly enhances data accessibility through its implementation of the FAIR Signposting Profile. This profile moves beyond simple human-readable metadata by embedding machine-actionable links within research data packaging. These links aren’t merely pointers; they function as explicit instructions, allowing automated agents and AI systems to navigate complex datasets and locate specific resources without manual intervention. By defining clear pathways for data discovery and access, the framework facilitates a dynamic exchange of information, enabling AI agents to autonomously explore, understand, and utilize research outputs. This capability is crucial for building truly interoperable AI systems where models can seamlessly connect with the data they need, fostering reproducibility and accelerating scientific progress.

The Model Context Protocol (MCP), built upon the Server-Sent Events (SSE) standard, establishes a dynamic communication channel for AI systems, moving beyond simple request-response interactions to enable ongoing, session-based resource discovery. Rather than repeatedly requesting information, MCP allows an AI agent to subscribe to updates regarding relevant data, models, and metadata as they become available or change. This continuous flow of information is particularly valuable in complex workflows where an agent needs to adapt to evolving contexts or explore interconnected resources. By leveraging SSE, MCP delivers these updates in a push-based manner, reducing latency and enabling a more responsive and efficient interaction between AI agents and data repositories. This approach not only streamlines resource discovery but also lays the groundwork for more sophisticated agentic AI systems capable of continuous learning and adaptation.

Performance evaluations reveal a trade-off between retrieval speed and enhanced functionality in accessing model information. While utilizing traditional REST APIs achieves remarkably swift model card retrieval – approximately 7.5 milliseconds – the Model Context Protocol (MCP) introduces latency, notably with larger model cards where database query responses can extend to 7843 milliseconds. This overhead, however, unlocks capabilities beyond simple data access; MCP facilitates the development of agentic AI systems and enables robust benchmarking that extends beyond the initial training phase. A layered approach combining REST and MCP further increases this overhead – approximately fourfold compared to a native MCP implementation – but provides a pathway for incremental adoption and compatibility with existing infrastructure, ultimately prioritizing interoperability and long-term AI system evolution.

The convergence of FAIR data principles with technologies like RO-Crate, the FAIR Signposting Profile, and the Model Context Protocol is fostering a new era of interoperable artificial intelligence. This synergy moves beyond simply sharing AI models and data; it establishes a framework for seamless reuse and integration. By packaging research objects with comprehensive metadata and providing machine-actionable links to resources, these systems enable AI agents to autonomously discover, access, and utilize information. This capability is critical not only for accelerating research but also for ensuring reproducibility and facilitating the development of AI systems that can learn and adapt from a broader, more readily available knowledge base. The result is an ecosystem where AI components function as building blocks, promoting innovation and reducing redundant effort across the scientific landscape.

The model card details the native MCP for a large model.

Towards Trustworthy and Accountable AI: A Path Forward

The increasing complexity of artificial intelligence and machine learning models demands robust methods for tracking their origins and evolution. To address this, researchers are integrating the PROV-ML ontology – a standardized vocabulary for representing provenance – with the Patra framework, a system designed for managing and analyzing ML pipelines. This combination facilitates the capture of detailed provenance information, essentially creating a comprehensive audit trail for each model. Such data includes the training datasets used, the algorithms applied, the parameters selected, and the computational resources utilized at each stage of development. By meticulously documenting this lifecycle, it becomes possible to understand why a model makes certain predictions, identify potential biases, and ensure accountability, ultimately fostering greater trust in AI systems and enabling effective debugging and reproducibility.

MLFieldPlanner establishes a dynamic cyberinfrastructure designed to dissect the complexities of machine learning pipelines and rigorously evaluate the performance implications of distributing workloads between edge devices and centralized cloud resources. This configurable system allows researchers and developers to model various deployment scenarios, meticulously tracking data flow, computational demands, and communication costs across the entire pipeline. By enabling detailed analysis of edge-cloud tradeoffs – considering factors like latency, bandwidth, and energy consumption – MLFieldPlanner facilitates the optimization of ML systems for specific application needs and resource constraints. The resulting insights are crucial for building efficient, scalable, and responsive AI solutions, particularly in environments where real-time processing and data privacy are paramount.

A comprehensive grasp of an AI model’s journey – from its initial training phases through deployment and sustained operation – is now achievable through the synergistic integration of provenance tracking and configurable cyberinfrastructure. By meticulously documenting each step of the model’s lifecycle, including data sources, algorithmic choices, and computational resources utilized, a detailed audit trail emerges. This allows for not only pinpointing the origins of model behavior, but also for evaluating performance shifts over time and identifying potential biases that may arise during real-world application. The resulting transparency fosters increased confidence in AI systems, enabling stakeholders to assess reliability, mitigate risks, and ensure responsible innovation across diverse domains.

The pursuit of artificial intelligence extends beyond mere computational power; a fundamental shift towards trustworthiness and accountability is now essential. By systematically integrating principles of provenance tracking, like those offered by PROV-ML and frameworks such as Patra, and by leveraging configurable cyberinfrastructure for pipeline analysis-as exemplified by MLFieldPlanner-developers can construct AI systems characterized by transparency. This holistic approach facilitates a comprehensive understanding of a model’s lifecycle, from its initial training data and algorithmic choices to its deployment and ongoing performance. Consequently, such AI systems are not simply capable of complex tasks, but are demonstrably aligned with ethical considerations and societal values, fostering confidence and responsible innovation in an increasingly AI-driven world.

The pursuit of robust AI/ML provenance, as detailed in the exploration of dynamic model cards within edge cyberinfrastructure, demands a commitment to verifiable foundations. Vinton Cerf aptly stated, “The Internet treats everyone the same.” This principle echoes the necessity for consistent, provable metadata access – whether through REST or Model Context Protocol – to ensure accountability throughout the model lifecycle. The article’s emphasis on tracking model usage and performance relies on a system where data isn’t simply ‘working on tests,’ but is demonstrably correct and consistently accessible, forming a logical, verifiable chain of evidence. Such rigorous standards are essential for building trust in agentic AI systems.

What’s Next?

The exercise of tethering dynamic model cards to edge AI cyberinfrastructure reveals, predictably, the gulf between declared intention and demonstrable truth. The performance comparison of REST and Model Context Protocol, while empirically useful, merely addresses a symptom. The fundamental problem remains: metadata, however efficiently transmitted, is only as reliable as its origin. A provenance graph, elegantly constructed, does not validate a model, only traces its lineage – a distinction of critical, yet often ignored, importance.

Future work must confront the issue of automated verification. Simply cataloging model usage is insufficient. The field requires formal methods to assess model drift, detect adversarial manipulation, and, crucially, establish provable guarantees regarding performance boundaries. The aspiration towards ‘agentic AI’ demands more than responsive systems; it necessitates systems whose behavior is demonstrably consistent with established mathematical principles.

The pursuit of ‘responsible AI’ risks becoming a purely performative exercise without such rigor. Knowledge graphs, while valuable for organization, are ultimately descriptive, not prescriptive. The next phase must focus on embedding formal verification directly into the model lifecycle, shifting the emphasis from tracking what a model does, to proving what it will do – or, failing that, clearly defining the limits of its reliability.

Original article: https://arxiv.org/pdf/2511.21661.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/