SYSTEM FOR DOCUMENTATION GENERATION AND LIFECYCLE MANAGEMENT OF ARTIFICIAL INTELLIGENCE MODELS

Abstract

ABSTRACT SYSTEM FOR DOCUMENTATION GENERATION AND LIFECYCLE MANAGEMENT OF ARTIFICIAL INTELLIGENCE MODELS The present disclosure introduces comprehensive documentation generation and lifecycle management system for AI models 100, incorporating several key components to automate and streamline the entire lifecycle. The system features a model documentation generator 102 that captures model architecture and hyperparameters. A version control module 104 manages versioning and ensures traceability of changes. Real-time performance is monitored by the lifecycle monitoring system 106, while the compliance and auditability engine 108 generates regulatory compliance documentation and audit trails. The bias detection and fairness monitoring module 112 ensures models meet ethical standards by documenting biases. A model retraining and change logging system 116 automates retraining processes and logs updates. Lastly, the dynamic documentation updates module 120 keeps model documentation current by updating it in real-time. The other key components are collaboration and access control system 110, bias detection and fairness monitoring module 112, explainability and interpretability tools 114, cross-platform integration layer 118. Reference Fig 1

Specification

Description:System for Documentation Generation and Lifecycle Management of Artificial Intelligence Models
TECHNICAL FIELD
[0001] The present innovation relates to systems and methods for automated documentation generation and lifecycle management of artificial intelligence (AI) models.

BACKGROUND

[0002] The rapid adoption of artificial intelligence (AI) across various industries has led to the development of increasingly complex models, which require effective lifecycle management. However, one of the key challenges faced by AI practitioners is the lack of standardized, comprehensive documentation throughout the model's lifecycle, from development and training to deployment and monitoring. Current practices often rely on manual documentation or fragmented tools, which can be time-consuming, error-prone, and insufficient for maintaining accountability, compliance, and collaboration. Users may turn to version control systems or project management tools to track changes and updates, but these options often lack the capability to automatically generate detailed documentation specific to AI models, such as architecture, training data, evaluation metrics, and hyperparameters.

[0003] The available systems also struggle with key drawbacks, including limited traceability of model changes, inadequate support for regulatory compliance, and poor collaboration features across multidisciplinary teams. Without proper documentation, AI models are difficult to interpret, explain, or audit, which poses risks in regulated industries like healthcare and finance where transparency is crucial. Additionally, the constant updates and retraining of models introduce challenges in managing multiple versions and ensuring reproducibility.
[0004] The present invention addresses these issues by offering an automated system for documentation generation and lifecycle management. Unlike existing systems, it integrates seamlessly into AI development workflows, automatically capturing and organizing all relevant model information. This system comprises features such as real-time monitoring, compliance support, and version control, ensuring complete traceability and accountability. The novelty of the invention lies in its ability to automate the entire documentation process, significantly reducing human error, improving operational efficiency, and ensuring regulatory compliance. With built-in explainability and collaboration tools, the invention empowers teams to manage AI models with greater transparency and reliability throughout their lifecycle.


OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to automate the generation of AI model documentation, reducing manual workload and improving operational efficiency.

[0006] Another object of the invention is to ensure traceability of AI model changes by integrating version control, enabling accurate tracking of model updates and modifications.

[0007] Another object of the invention is to enhance compliance with regulatory standards by providing real-time documentation and audit trails for AI models, ensuring transparency and accountability.

[0008] Another object of the invention is to promote collaboration across multidisciplinary teams by offering a shared platform with role-based access to AI model documentation and lifecycle data.

[0009] Another object of the invention is to improve the explainability and interpretability of AI models by automatically generating detailed reports that include model architecture, training data, and performance metrics.

[00010] Another object of the invention is to facilitate the monitoring and maintenance of AI models by integrating real-time performance tracking and automatic retraining triggers when model performance deteriorates.

[00011] Another object of the invention is to address the limitations of current AI lifecycle management tools by providing a unified system that integrates documentation, version control, and compliance features.

[00012] Another object of the invention is to reduce the risk of errors in AI model management by automating documentation workflows and ensuring all lifecycle events are accurately recorded.

[00013] Another object of the invention is to support AI model governance by offering audit trails and compliance reports that align with industry-specific regulations and ethical AI standards.

[00014] Another object of the invention is to provide a scalable, customizable framework that can be easily integrated into existing AI development environments, supporting cross-platform compatibility and industry-specific documentation needs


SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, system for documentation generation of artificial intelligence model is presented. It provides an automated system for generating and managing comprehensive documentation throughout the lifecycle of AI models. It integrates features such as version control, real-time monitoring, compliance management, and collaboration tools, ensuring traceability, transparency, and accountability. By automating the documentation process, the invention reduces manual effort, enhances operational efficiency, and ensures compliance with regulatory standards. The system also promotes better collaboration across multidisciplinary teams, making AI models more explainable and reliable.

[00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow.

[00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims.

BRIEF DESCRIPTION OF DRAWINGS
[00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers.

[00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00020] FIG. 1 is component wise drawing for system for documentation generation and lifecycle management of artificial intelligence model.

[00021] FIG 2 is working methodology of system for documentation generation and lifecycle management of artificial intelligence model.

DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of system for documentation generation and lifecycle management of artificial intelligence model and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, system for documentation generation and lifecycle management of artificial intelligence model 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of model documentation generator 102, version control module 104, lifecycle monitoring system 106, compliance and auditability engine 108, collaboration and access control system 110, bias detection and fairness monitoring module 112, explainability and interpretability tools 114, model retraining and change logging system 116, cross-platform integration layer 118 and dynamic documentation updates module 120.

[00029] Referring to Fig. 1, the present disclosure provides details of system for documentation generation and lifecycle management of artificial intelligence model 100. The system automates the creation, updating, and maintenance of AI model documentation using components like model documentation generator 102 and version control module 104. It includes lifecycle monitoring system 106 for real-time performance tracking and compliance and auditability engine 108 for regulatory adherence. The collaboration and access control system 110 facilitates secure, role-based access to documentation, while bias detection and fairness monitoring module 112 ensures ethical AI practices. Additionally, components like model retraining and change logging system 116 and dynamic documentation updates module 120 offer real-time updates and transparency across the AI model's lifecycle.

[00030] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with model documentation generator 102, which plays a crucial role in automatically capturing key details throughout the AI model lifecycle. This component works by extracting information such as model architecture, training data, hyperparameters, and performance metrics from development environments. The model documentation generator 102 interacts with version control module 104 to ensure that each version of the model is accurately documented, making sure all changes are logged seamlessly and without manual intervention. Additionally, it updates the documentation in real-time based on changes captured by lifecycle monitoring system 106.

[00031] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with version control module 104, which handles the versioning of AI models, ensuring traceability across the model lifecycle. This module assigns unique version identifiers to each model iteration and tracks changes made to the architecture, hyperparameters, and datasets. Working closely with model documentation generator 102, it ensures that every version's documentation is linked with the corresponding model. Moreover, the version control module 104 integrates with lifecycle monitoring system 106 to maintain a comprehensive history of model performance across versions, facilitating seamless updates and retraining when needed.

[00032] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with lifecycle monitoring system 106, which continuously tracks the AI model's performance post-deployment, monitoring key metrics such as accuracy, bias, and drift. This system provides real-time alerts when performance drops below a specified threshold or when bias is detected, enabling prompt intervention. The lifecycle monitoring system 106 works in conjunction with the compliance and auditability engine 108 to ensure that all performance metrics and adjustments are documented, making them available for audit purposes. It also triggers the model retraining and change logging system 116 when performance degradation is identified, ensuring the model remains reliable over time.

[00033] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with compliance and auditability engine 108, which ensures the AI model adheres to regulatory and ethical standards throughout its lifecycle. This component automatically generates compliance documentation, including transparency reports and audit trails, which are essential for adhering to industry regulations such as GDPR. The compliance and auditability engine 108 is closely integrated with the version control module 104 and model documentation generator 102, ensuring that every model version and modification is logged and available for review by regulatory bodies. Additionally, it coordinates with lifecycle monitoring system 106 to track model behavior, ensuring that any issues are well-documented and addressed in a timely manner.

[00034] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with collaboration and access control system 110, which facilitates secure, role-based access to AI models and their documentation. This system allows multiple stakeholders, including data scientists, business analysts, and regulatory teams, to access the relevant parts of the model documentation based on their roles. The collaboration and access control system 110 integrates with model documentation generator 102 to provide real-time documentation access while ensuring sensitive information remains protected. Additionally, it works with compliance and auditability engine 108 to ensure that all collaborative activities and changes are recorded for compliance purposes

[00035] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with bias detection and fairness monitoring module 112, which continuously evaluates the AI model for any bias or fairness issues during its operation. This component is essential in ensuring ethical AI practices by flagging potential biases in the model's decision-making processes. The bias detection and fairness monitoring module 112 works closely with lifecycle monitoring system 106 to monitor real-time performance data and detect deviations that could indicate fairness concerns. In the event of detected bias, the module triggers actions through the compliance and auditability engine 108, ensuring proper documentation of the issue and steps taken to remediate it.

[00036] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with explainability and interpretability tools 114, which enhance the transparency of AI models by providing intuitive visualizations and reports that explain how the model arrives at its decisions. These tools generate insights into model behavior, feature importance, and decision logic, making the system accessible to both technical and non-technical stakeholders. The explainability and interpretability tools 114 are integrated with model documentation generator 102 to ensure that every generated explanation is documented and linked to the specific model version. This component also collaborates with bias detection and fairness monitoring module 112 to ensure the model's explanations align with fairness and ethical standards.

[00037] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with model retraining and change logging system 116, which automates the process of retraining AI models when performance declines or when new data becomes available. This component works by initiating retraining processes and logging all changes made to the model, such as updated hyperparameters or modified datasets. The model retraining and change logging system 116 operates in conjunction with lifecycle monitoring system 106, which identifies when retraining is needed due to performance degradation. It also integrates with version control module 104 to ensure that each retraining event and corresponding model version is fully documented and traceable.

[00038] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with cross-platform integration layer 118, which enables seamless integration with popular AI development environments, such as Jupyter, TensorFlow, and PyTorch. This component ensures that the system can be easily adopted into existing AI workflows without disrupting current processes. The cross-platform integration layer 118 works closely with model documentation 102 generator 102 and version control module 104 to automatically capture relevant model data during development and training, ensuring that all versions and updates are accurately recorded. It also facilitates the flow of data between the AI environment and the documentation system, promoting efficient lifecycle management.

[00039] Referring to Fig. 1, system for documentation generation and lifecycle management of AI models 100 is provided with dynamic documentation updates module 120, which ensures that AI model documentation is updated in real-time as new data or performance changes are detected. This module works by dynamically adjusting the documentation based on live feedback from lifecycle monitoring system 106 and model performance data. The dynamic documentation updates module 120 ensures that the documentation reflects the most current state of the AI model, including changes to model architecture, training data, or hyperparameters. It also collaborates with model documentation generator 102 to streamline the documentation process, ensuring that all updates are captured and recorded promptly.


[00040] Referring to Fig 2, there is illustrated method 200 for system for documentation generation and lifecycle management of AI models 100. The method comprises:

At step 202, method 200 includes the system activating the model documentation generator 102 to automatically capture initial model architecture and training data;

At step 204, method 200 includes the version control module 104 assigning a unique version identifier to the model and linking it with the corresponding documentation;

At step 206, method 200 includes the lifecycle monitoring system 106 tracking the AI model's real-time performance, capturing metrics such as accuracy and model drift;

At step 208, method 200 includes the compliance and auditability engine 108 generating regulatory compliance documentation, including transparency reports and audit trails for the model;

At step 210, method 200 includes the bias detection and fairness monitoring module 112 continuously checking for potential biases in the model's decision-making process and documenting any detected issues;

At step 212, method 200 includes the model retraining and change logging system 116 initiating a retraining event based on performance degradation or new data availability, while logging all updates;

At step 214, method 200 includes the dynamic documentation updates module 120 dynamically updating the AI model's documentation in real-time as performance metrics or model behavior changes;

At step 216, method 200 includes the explainability and interpretability tools 114 generating visual reports and insights into how the model arrives at its decisions, and linking these reports to the corresponding version of the model;

At step 218, method 200 includes the collaboration and access control system 110 enabling role-based access for various stakeholders to view or update model documentation securely;

At step 220, method 200 includes the cross-platform integration layer 118 ensuring seamless integration of the system with AI development environments, capturing relevant updates as the model evolves.

[00041] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00042] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00043] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

, Claims:WE CLAIM:
1. A system for documentation generation and lifecycle management of AI models of artificial intelligence model 100 comprising of
model documentation generator 102 to automatically capture and generate AI model documentation;
version control module 104 to track and manage model versions with complete traceability;
lifecycle monitoring system 106 to monitor real-time AI model performance and detect issues like drift or bias;
compliance and auditability engine 108 to generate regulatory documentation and maintain audit trails;
collaboration and access control system 110 to enable secure role-based access and facilitate team collaboration;
bias detection and fairness monitoring module 112 to continuously monitor and address any potential biases in model outputs;
explainability and interpretability tools 114 to provide visual insights and explanations for model decision-making processes;
model retraining and change logging system 116 to automate retraining and log all updates and changes to the model;
cross-platform integration layer 118 to ensure seamless integration with AI development environments; and
dynamic documentation updates module 120 to update model documentation in real-time as performance changes occur.

2. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein model documentation generator 102 is configured to automatically capture and generate comprehensive documentation, including model architecture, training data, hyperparameters, and performance metrics throughout the AI model lifecycle.

3. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein version control module 104 is configured to track and manage different versions of the AI model, ensuring full traceability by linking model changes to the corresponding documentation for each version.

4. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein lifecycle monitoring system 106 is configured to monitor real-time performance metrics, such as model accuracy, bias detection, and drift, and trigger alerts when performance degradation is identified.

5. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein compliance and auditability engine 108 is configured to generate regulatory compliance documentation, maintain audit trails, and provide transparency and explainability reports for regulatory and ethical review.

6. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein collaboration and access control system 110 is configured to enable secure, role-based access to the AI model and its documentation, facilitating collaboration among stakeholders while maintaining the integrity and security of sensitive information.

7. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein bias detection and fairness monitoring module 112 is configured to continuously assess the AI model for potential bias and fairness concerns, documenting issues and remediation steps to ensure compliance with ethical AI standards.

8. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein model retraining and change logging system 116 is configured to automate the retraining of AI models based on new data or performance issues, while logging and documenting all changes made during the retraining process.

9. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein dynamic documentation updates module 120 is configured to update AI model documentation in real-time based on performance metrics, model changes, and newly identified data, ensuring that the documentation remains current and accurate.

10. The system for documentation generation and lifecycle management system for AI models 100 as claimed in claim 1, wherein method comprises of
system activating the model documentation generator 102 to automatically capture initial model architecture and training data;
version control module 104 assigning a unique version identifier to the model and linking it with the corresponding documentation;
lifecycle monitoring system 106 tracking the AI model's real-time performance, capturing metrics such as accuracy and model drift;
compliance and auditability engine 108 generating regulatory compliance documentation, including transparency reports and audit trails for the model;
bias detection and fairness monitoring module 112 continuously checking for potential biases in the model's decision-making process and documenting any detected issues;
model retraining and change logging system 116 initiating a retraining event based on performance degradation or new data availability, while logging all updates;
dynamic documentation updates module 120 dynamically updating the AI model's documentation in real-time as performance metrics or model behavior changes;
explainability and interpretability tools 114 generating visual reports and insights into how the model arrives at its decisions, and linking these reports to the corresponding version of the model;
collaboration and access control system 110 enabling role-based access for various stakeholders to view or update model documentation securely; and
cross-platform integration layer 118 ensuring seamless integration of the system with AI development environments capturing relevant updates as the model evolves.

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