SYSTEM AND METHOD FOR AUTOMATED AI PIPELINE REQUIREMENT MANAGEMENT

Abstract

ABSTRACT SYSTEM AND METHOD FOR AUTOMATED AI PIPELINE REQUIREMENT MANAGEMENT The present disclosure introduces system and method for automated AI pipeline requirement management 100 that enhances efficiency, traceability, compliance, and collaboration throughout the AI/ML lifecycle. The system comprises of requirement definition module 102 for capturing project requirements, collaboration platform 104 to enable stakeholder communication, traceability engine 106, compliance tracking module 108 , performance metrics management module 110, integration APIs and connectors 112, AI-powered requirement analysis tool 114, version control system 116, user role management and access control module 118, feedback loop and continuous improvement mechanism 120, customizable templates for requirement definition 122, multi-layer requirement hierarchy structure 124, real-time stakeholder impact analysis tool 126, automated dependency resolution module 128, integrated risk management module 130, intelligent requirement forecasting system 132, user-friendly visualization tools 134, historical requirement analytics module 136, mobile access and notification system 138, multi-project management capability 140 and customizable compliance framework integration 142. Reference Fig 1

Specification

Description:SYSTEM AND METHOD FOR AUTOMATED AI PIPELINE REQUIREMENT MANAGEMENT
TECHNICAL FIELD
[0001] The present innovation relates to an automated system and method for managing AI pipeline requirements, including data specifications, model criteria, and compliance metrics, throughout the AI/ML development lifecycle.

BACKGROUND

[0002] The rapid adoption of artificial intelligence (AI) and machine learning (ML) across industries such as healthcare, finance, and transportation has led to the increasing complexity of AI pipelines, which involve multiple stages, including data collection, preprocessing, model training, evaluation, and deployment. Managing the requirements for each stage-such as data specifications, model performance criteria, and regulatory compliance-has become a major challenge. Current practices rely on spreadsheets, project management tools, or documentation platforms, which are not well-suited to handle the dynamic and evolving nature of AI projects. These methods lack robust traceability, making it difficult to track changes in requirements, manage dependencies, and align evolving models with business objectives. Additionally, the absence of effective collaboration platforms complicates communication among diverse stakeholders, including data scientists, engineers, and regulatory experts, leading to project delays, misunderstandings, and scope creep.

[0003] Existing solutions, such as generic project management tools, provide only basic requirement tracking and often fail to integrate with AI/ML development workflows. They also do not support compliance monitoring, making it harder to manage regulatory obligations. The absence of automated performance tracking further hinders the timely evaluation of AI models. These limitations result in inefficiencies, increased risks, and inconsistent project outcomes.

[0004] The present invention addresses these gaps by providing a novel automated system and method for AI pipeline requirement management. It integrates requirement tracking directly into the AI development lifecycle, enabling real-time traceability, automated compliance checks, and performance metric monitoring through customizable dashboards. Unlike existing solutions, this invention offers seamless API-based integration with AI frameworks like TensorFlow and PyTorch, ensuring that requirements align with project progress. Key features include an AI-powered requirement analysis module, version control for requirement changes, and role-based access management, fostering collaboration and accountability. This invention enhances project efficiency, reduces human error, and ensures compliance, setting it apart from conventional tools. Its ability to streamline AI pipeline management makes it a valuable solution for organizations seeking scalable, reliable, and transparent AI development
OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to enhance the functionality of electronic devices by incorporating AI-controlled adaptive features for real-time adjustments based on user behavior and environmental conditions.

[0006] Another object of the invention is to improve energy efficiency in electronic apparatuses by employing self-optimizing energy management systems that reduce power consumption while maintaining high performance.

[0007] Another object of the invention is to provide a personalized user experience by integrating AI algorithms that tailor device operations to individual preferences and usage patterns.

[0008] Another object of the invention is to ensure seamless interoperability with other smart devices and systems, facilitating efficient data sharing and coordinated operations across different environments.
[0009] Another object of the invention is to enhance user convenience through predictive analytics that anticipate user needs and automatically adjust device settings accordingly.

[00010] Another object of the invention is to improve the adaptability of electronic devices by incorporating multi-modal interaction capabilities such as voice commands, touch gestures, and visual cues.

[00011] Another object of the invention is to provide context-aware functionalities, allowing the device to autonomously adjust its operations based on real-time environmental data and situational factors.

[00012] Another object of the invention is to support sustainability goals by offering a device that minimizes energy waste and aligns with environmental concerns through adaptive energy usage.

[00013] Another object of the invention is to enhance user safety and health by integrating AI-powered monitoring and adaptive response systems, especially in healthcare and wellness applications.

[00014] Another object of the invention is to offer a flexible and scalable system that can be applied across various industries, including consumer electronics, automotive, healthcare, home automation, and industrial systems.

[00015] The primary object of the invention is to automate the management of requirements throughout the lifecycle of AI pipelines, enhancing efficiency and accuracy.

[00016] Another object of the invention is to provide real-time traceability of requirements, ensuring alignment between evolving models, datasets, and business objectives.

[00017] Another object of the invention is to facilitate seamless collaboration among diverse stakeholders, such as data scientists, engineers, and regulatory experts, through an integrated platform.

[00018] Another object of the invention is to ensure compliance with regulatory standards by incorporating automated compliance checks and alerts directly into the workflow.

[00019] Another object of the invention is to integrate requirement management with popular AI frameworks like TensorFlow and PyTorch, promoting a synchronized development process.

[00020] Another object of the invention is to improve performance management by automating the collection and visualization of key performance metrics through customizable dashboards.

[00021] Another object of the invention is to minimize human error by using AI-powered tools to analyze, extract, and classify requirements from various textual sources.

[00022] Another object of the invention is to enhance accountability through version control and role-based access management for tracking historical changes and managing stakeholder permissions.

[00023] Another object of the invention is to enable continuous improvement by capturing feedback, lessons learned, and insights from past projects to refine future requirements.

[00024] Another object of the invention is to offer scalability by supporting multi-project management, providing a consolidated view of requirements across multiple AI projects and enabling efficient resource allocation

SUMMARY OF THE INVENTION

[00025] In accordance with the different aspects of the present invention, system and method for automated AI pipeline requirement management is presented. It enables real-time tracking, collaboration, and compliance monitoring throughout the AI/ML lifecycle. It integrates with popular AI frameworks to align requirements with development workflows and automates performance metric collection using customizable dashboards. The system employs AI-powered analysis for requirement extraction, version control for traceability, and role-based access management for secure collaboration. By addressing challenges like regulatory compliance and stakeholder coordination, the invention enhances the efficiency and reliability of AI projects. It also supports continuous improvement through feedback loops and offers scalability for managing multiple projects simultaneously.

[00026] 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.

[00027] 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
[00028] 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.

[00029] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein:

[00030] FIG. 1 is component wise drawing for system and method for automated AI pipeline requirement management
[00031] FIG 2 is working methodology of system and method for automated AI pipeline requirement management.


DETAILED DESCRIPTION

[00032] 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.

[00033] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of system and method for automated AI pipeline requirement management 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.

[00034] 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.

[00035] 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.

[00036] 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.

[00037] 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.

[00038] Referring to Fig. 1, system and method for automated AI pipeline requirement management 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of requirement definition module 102, collaboration platform 104, traceability engine 106, compliance tracking module 108, performance metrics management module 110, integration APIs and connectors 112, AI-powered requirement analysis tool 114, version control system 116, user role management and access control module 118, feedback loop and continuous improvement mechanism 120, customizable templates for requirement definition 122, multi-layer requirement hierarchy structure 124, real-time stakeholder impact analysis tool 126, automated dependency resolution module 128, integrated risk management module 130, intelligent requirement forecasting system 132, user-friendly visualization tools 134, historical requirement analytics module 136, mobile access and notification system 138, multi-project management capability 140 and customizable compliance framework integration 142.

[00039] Referring to Fig. 1, the present disclosure provides details of system and method for automated AI pipeline requirement management 100 which is designed to streamline the definition, tracking, and compliance of requirements throughout the AI/ML development lifecycle. It features real-time traceability engine 106, collaboration platform 104, and compliance tracking module 108 to ensure seamless project management and regulatory adherence. The system integrates with popular AI frameworks through integration APIs and connectors 112 for synchronized workflows and uses AI-powered requirement analysis tool 114 to extract and classify requirements from textual data. It comprises of key components like requirement definition module 102, traceability engine 106, performance metrics management module 110, version control system 116, and feedback loop and continuous improvement mechanism 120. It supports multi-project management capability 140, provides user-friendly visualization tools 134, and offers mobile access and notification system 138 for real-time updates and alerts. The invention enhances efficiency, promotes continuous improvement, and ensures alignment of requirements with project objectives, making it an ideal solution for scalable, effective AI development.

[00040] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with requirement definition module 102, which enables users to define and categorize requirements based on type, priority, and project phase. It allows the input of data specifications, functional/non-functional requirements, and compliance standards. This component works closely with collaboration platform 104 by sharing defined requirements for review and approval. It also links with the traceability engine 106 to establish dependencies between requirements, ensuring they are tracked throughout the project lifecycle.

[00041] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with collaboration platform 104, which fosters communication among stakeholders by offering a shared environment for discussions, comments, and feedback on requirements. It enables task assignments, deadlines, and real-time progress tracking. The collaboration platform 104 integrates with requirement definition module 102 to capture stakeholder inputs and modifications. It also works with version control system 116 to manage different iterations of requirements, maintaining a clear history of changes.

[00042] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with traceability engine 106, which automatically maps and monitors interdependencies among requirements throughout the lifecycle of the AI project. It visualizes how changes in one requirement affect others, ensuring alignment with project goals. The traceability engine 106 interacts with compliance tracking module 108 to monitor regulatory requirements and track their impact across the pipeline. It also works closely with real-time stakeholder impact analysis tool 126 to provide insights on requirement changes.

[00043] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with compliance tracking module 108, which integrates regulatory checks and audits into the workflow to ensure adherence to standards and policies. It generates automated alerts when compliance milestones approach or deviations occur. The compliance tracking module 108 collaborates with performance metrics management module 110 to align performance criteria with regulatory benchmarks. It also shares data with customizable compliance framework integration 142 for flexibility in meeting specific industry standards.

[00044] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with performance metrics management module 110, which defines, monitors, and evaluates key performance indicators (KPIs) for AI models, such as accuracy and recall. It automates the collection of performance data during model evaluation and displays it via dashboards. The performance metrics management module 110 works in tandem with integration APIs and connectors 112 to extract data from AI development tools and shares this information with feedback loop and continuous improvement mechanism 120 for iterative enhancement

[00045] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with integration APIs and connectors 112, which enable seamless data flow between the requirement management system and popular AI frameworks like TensorFlow and PyTorch. This component ensures that evolving requirements remain aligned with development progress. It interacts with traceability engine 106 to keep dependencies up-to-date and supports multi-project management capability 140 by synchronizing data across multiple projects

[00046] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with AI-powered requirement analysis tool 114, which uses natural language processing (NLP) to extract, classify, and analyze requirements from various sources, such as meeting minutes and documents. It accelerates requirement gathering and improves the accuracy of requirement capture. This tool feeds analyzed data to requirement definition module 102 and aligns extracted requirements with the compliance tracking module 108 to ensure adherence to regulations from the start

[00047] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with version control system 116, which tracks historical changes to requirements, allowing users to compare, revert, and restore previous versions. It ensures transparency and accountability in requirement management. This system integrates with collaboration platform 104 to manage multiple iterations and synchronizes with feedback loop and continuous improvement mechanism 120 to incorporate lessons learned into future versions

[00048] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with user role management and access control module 118, which defines permissions for stakeholders based on their roles, such as project managers or data scientists. It ensures secure collaboration by restricting access to sensitive information while allowing authorized users to contribute. This module interfaces with collaboration platform 104 for assigning tasks and integrates with feedback loop and continuous improvement mechanism 120 to capture user feedback effectively.

[00049] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with feedback loop and continuous improvement mechanism 120, which captures user insights and lessons learned from completed projects to refine future requirements. It enables iterative enhancement of processes by integrating with performance metrics management module 110 for data-driven adjustments. This mechanism also interacts with historical requirement analytics module 136 to identify recurring issues and improve project outcomes over time.

[00050] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with customizable templates for requirement definition 122, which offer predefined structures for functional, non-functional, and compliance-related requirements. These templates can be tailored to specific project needs. The templates align with requirement definition module 102 to standardize requirement entry and enhance usability

[00051] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with multi-layer requirement hierarchy structure 124, which categorizes requirements into strategic, tactical, and operational levels. This structure improves alignment with business objectives and supports prioritization. It interfaces with traceability engine 106 to visualize the relationships across different levels of requirements.

[00052] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with real-time stakeholder impact analysis tool 126, which assesses how changes in requirements affect stakeholders and project outcomes. This tool provides insights into potential risks, working closely with traceability engine 106 to map dependencies and integrated risk management module 130 to develop mitigation strategies.

[00053] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with automated dependency resolution module 128, which identifies and resolves conflicts among requirements, streamlining project workflows. It interacts with traceability engine 106 for dependency mapping and aligns with version control system 116 to manage changes effectively.

[00054] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with integrated risk management module 130, which identifies, tracks, and mitigates risks associated with requirements. It integrates with real-time stakeholder impact analysis tool 126 for proactive risk management and aligns with feedback loop and continuous improvement mechanism 120 to adjust strategies based on project outcomes.

[00055] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with intelligent requirement forecasting system 132, which uses machine learning algorithms to predict future requirement changes based on historical data and stakeholder inputs. It supports strategic planning by aligning forecasts with multi-project management capability 140 for resource allocation.

[00056] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with user-friendly visualization tools 134, which allow stakeholders to create custom dashboards for tracking requirements, performance metrics, and project milestones. These tools enhance decision-making and work closely with performance metrics management module 110 to visualize KPIs.

[00057] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with historical requirement analytics module 136, which enables organizations to analyze past requirement trends to identify patterns and areas for improvement. It integrates with feedback loop and continuous improvement mechanism 120 to refine future requirements based on insights from previous projects.

[00058] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with mobile access and notification system 138, which offers stakeholders real-time updates on requirement changes, compliance alerts, and milestones. It works in tandem with collaboration platform 104 to keep teams informed and aligned, regardless of location.

[00059] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with multi-project management capability 140, which allows users to manage requirements across multiple AI projects simultaneously. It offers a consolidated view for efficient resource allocation and integrates with integration APIs and connectors 112 to maintain synchronization across projects.

[00060] Referencing to Fig 1, system and method for automated AI pipeline requirement management 100 is provided with customizable compliance framework integration 142, which allows organizations to adapt compliance tracking based on industry-specific regulations. It aligns with compliance tracking module 108 to ensure adherence and supports flexibility in regulatory management


[00061] Referring to Fig 2, there is illustrated method 200 for system and method for automated AI pipeline requirement management 100. The method comprises:

At step 202, method 200 includes users defining project requirements through the requirement definition module 102, specifying data specifications, performance metrics, and compliance criteria;

At step 204, method 200 includes stakeholders collaborating through the collaboration platform 104 to review, modify, and approve the defined requirements, facilitating discussions and assigning tasks with deadlines;

At step 206, method 200 includes the traceability engine 106 mapping dependencies between requirements and tracking changes throughout the AI pipeline lifecycle to ensure alignment across project phases;

At step 208, method 200 includes the compliance tracking module 108 monitoring regulatory criteria and issuing automated alerts for upcoming audits or deviations from compliance standards;

At step 210, method 200 includes the performance metrics management module 110 collecting and visualizing real-time performance data during model evaluation, allowing stakeholders to assess model accuracy and other KPIs;

At step 212, method 200 includes the integration APIs and connectors 112 seamlessly connecting the system with AI development tools like TensorFlow and PyTorch, keeping requirements synchronized with model development;

At step 214, method 200 includes the AI-powered requirement analysis tool 114 using NLP algorithms to extract and classify requirements from documents, meeting minutes, and other sources to accelerate requirement gathering;

At step 216, method 200 includes the version control system 116 tracking changes to requirements and maintaining a history of previous versions for easy comparison and restoration;

At step 218, method 200 includes the user role management and access control module 118 defining permissions based on user roles, ensuring secure collaboration while managing sensitive information;

At step 220, method 200 includes the feedback loop and continuous improvement mechanism 120 capturing insights from completed projects and refining future requirements based on lessons learned;

At step 222, method 200 includes the multi-layer requirement hierarchy structure 124 organizing requirements into strategic, tactical, and operational levels to enhance prioritization and alignment with business objectives;

At step 224, method 200 includes the automated dependency resolution module 128 detecting and resolving conflicts between requirements, reducing manual intervention and enhancing project efficiency;

At step 226, method 200 includes the intelligent requirement forecasting system 132 predicting future requirement changes based on historical data and stakeholder inputs to aid in strategic planning;

At step 228, method 200 includes user-friendly visualization tools 134 displaying dashboards and reports that track project milestones, compliance metrics, and requirement statuses to facilitate decision-making;

At step 230, method 200 includes mobile access and notification system 138 sending real-time alerts and updates to stakeholders, ensuring they remain informed about project developments;

At step 232, method 200 includes multi-project management capability 140 enabling users to manage requirements across multiple projects, streamlining resource allocation and prioritization;

At step 234, method 200 includes customizable compliance framework integration 142 adapting compliance tracking based on industry-specific regulations, maintaining flexibility while ensuring adherence to regulatory requirements.

[00062] 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.

[00063] 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.

[00064] 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. The system and method for automated AI pipeline requirement management 100 comprising of
requirement definition module 102 to enable users to define and categorize requirements based on project needs and priorities;
collaboration platform 104 to facilitate communication and task management among stakeholders in real-time;
traceability engine 106 to map dependencies and track changes in requirements throughout the project lifecycle;
compliance tracking module 108 to monitor regulatory criteria and issue alerts for compliance-related milestones;
performance metrics management module 110 to collect and visualize key performance indicators during model evaluation;
integration apis and connectors 112 to connect seamlessly with ai development frameworks like tensorflow and pytorch;
AI-powered requirement analysis tool 114 to extract and classify requirements from documents using nlp algorithms;
version control system 116 to maintain a history of changes and allow comparisons across requirement iterations;
user role management and access control module 118 to define permissions and ensure secure collaboration among users;
feedback loop and continuous improvement mechanism 120 to capture insights and refine future project requirements;
customizable templates for requirement definition 122 to offer predefined structures for various types of requirements;

multi-layer requirement hierarchy structure 124 to organize requirements into strategic, tactical, and operational levels;
real-time stakeholder impact analysis tool 126 to assess the effect of requirement changes on stakeholders and outcomes;
automated dependency resolution module 128 to identify and resolve conflicts among project requirements;
integrated risk management module 130 to track and mitigate risks associated with specific requirements;
intelligent requirement forecasting system 132 to predict future requirement changes based on historical trends and inputs;
user-friendly visualization tools 134 to create dashboards and reports displaying project metrics and milestones;
historical requirement analytics module 136 to analyze past trends and performance for continuous learning;
mobile access and notification system 138 to provide real-time updates and alerts to stakeholders on the go;
multi-project management capability 140 to manage requirements across multiple projects for efficient resource allocation and
customizable compliance framework integration 142 to adapt compliance tracking to specific regulatory environments.

2. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein requirement definition module 102 defines and categorizes requirements based on project needs and is integrated with collaboration platform 104 to facilitate real-time stakeholder communication and task management.

3. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein traceability engine 106 maps dependencies among requirements and tracks changes throughout the lifecycle, ensuring alignment with project objectives and milestones.

4. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein compliance tracking module 108 monitors regulatory criteria and generates automated alerts for compliance deviations or upcoming audits within the AI development workflow.

5. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein performance metrics management module 110 collects and visualizes key performance indicators (KPIs) during model evaluation and aligns them with defined benchmarks and project requirements.

6. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein integration APIs and connectors 112 to synchronize data and ensure seamless interaction with AI development frameworks keeping requirements aligned with model progress.

7. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein AI-powered requirement analysis tool 114 uses natural language processing (NLP) to extract, classify, and organize requirements from unstructured textual data such as meeting minutes and documents.

8. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein version control system 116 tracks historical changes, manages iterations, and provides restoration capabilities across different versions of project requirements.

9. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein feedback loop and continuous improvement mechanism 120 captures project insights, refines future requirements, and enhances performance by integrating feedback and lessons learned from previous implementations.

10. The system and method for automated AI pipeline requirement management 100 as claimed in claim 1, wherein method comprises of
users defining project requirements through the requirement definition module 102, specifying data specifications, performance metrics, and compliance criteria;
stakeholders collaborating through the collaboration platform 104 to review, modify, and approve the defined requirements, facilitating discussions and assigning tasks with deadlines;
traceability engine 106 mapping dependencies between requirements and tracking changes throughout the AI pipeline lifecycle to ensure alignment across project phases;
compliance tracking module 108 monitoring regulatory criteria and issuing automated alerts for upcoming audits or deviations from compliance standards;
performance metrics management module 110 collecting and visualizing real-time performance data during model evaluation, allowing stakeholders to assess model accuracy and other KPIs;
integration APIs and connectors 112 seamlessly connecting the system with AI development tools like TensorFlow and PyTorch, keeping requirements synchronized with model development;
AI-powered requirement analysis tool 114 using NLP algorithms to extract and classify requirements from documents, meeting minutes, and other sources to accelerate requirement gathering;
version control system 116 tracking changes to requirements and maintaining a history of previous versions for easy comparison and restoration;
user role management and access control module 118 defining permissions based on user roles, ensuring secure collaboration while managing sensitive information;
feedback loop and continuous improvement mechanism 120 capturing insights from completed projects and refining future requirements based on lessons learned;
multi-layer requirement hierarchy structure 124 organizing requirements into strategic, tactical, and operational levels to enhance prioritization and alignment with business objectives;
automated dependency resolution module 128 detecting and resolving conflicts between requirements, reducing manual intervention and enhancing project efficiency;
intelligent requirement forecasting system 132 predicting future requirement changes based on historical data and stakeholder inputs to aid in strategic planning;
user-friendly visualization tools 134 displaying dashboards and reports that track project milestones, compliance metrics, and requirement statuses to facilitate decision-making;
mobile access and notification system 138 sending real-time alerts and updates to stakeholders, ensuring they remain informed about project developments;
multi-project management capability 140 enabling users to manage requirements across multiple projects, streamlining resource allocation and prioritization; and
customizable compliance framework integration 142 adapting compliance tracking based on industry-specific regulations, maintaining flexibility while ensuring adherence to regulatory requirements.

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