AI MODEL CONTROL FRAMEWORK FOR OPTIMIZED PERFORMANCE IN AI SYSTEMS

Abstract

ABSTRACT AI MODEL CONTROL FRAMEWORK FOR OPTIMIZED PERFORMANCE IN AI SYSTEMS The present disclosure introduces an AI model control framework for optimized performance in AI systems 100, which enhances the adaptability of AI models through real-time monitoring and adaptive mechanisms. The system comprises of monitoring module 102 to track performance metrics, evaluation engine 104 to analyze data and detect drift, and adaptation mechanism 106 to adjust model parameters dynamically. A feedback loop 108 captures the outcomes of these adjustments, enabling continuous learning. The real-time drift detection system 110 identifies data distribution shifts, while the resource optimization module 112 reduces computational resources during training and inference. The user interface 114 provides real-time performance metrics The integration layer 116 ensures compatibility with existing AI infrastructures, supporting both cloud and edge computing environments. Security mechanisms 118 protects against adversarial attacks and scalability architecture 120 manages multiple AI models concurrently. Lastly, cloud and edge computing support 122 enables flexible deployment across platforms. Reference Fig 1

Specification

Description:AI Model Control Framework for Optimized Performance in AI Systems
TECHNICAL FIELD
[0001] The present innovation relates to an AI model control framework for optimizing the performance of artificial intelligence systems through real-time monitoring, adaptive algorithms, and control mechanisms.

BACKGROUND

[0002] The rapid advancement of artificial intelligence (AI) technologies has transformed numerous industries by enhancing automation and decision-making processes. However, as AI models become more complex, several challenges have emerged that hinder their performance and scalability. One major issue is model drift, where the accuracy and reliability of AI models degrade over time due to changing data patterns. This often requires frequent retraining and manual adjustments, which are both time-consuming and resource-intensive. Users currently address these issues through periodic retraining, manual parameter adjustments, or monitoring, but these methods are limited in their ability to respond quickly to real-time shifts. Moreover, computational inefficiencies in AI models, especially those requiring significant processing power like deep learning models, further exacerbate the problem by increasing operational costs and energy consumption.

[0003] Existing solutions, such as retraining models or adding more computational resources, are costly and inefficient. Additionally, many systems lack automated real-time adaptation mechanisms, leaving users with limited control over how and when to intervene. These traditional approaches are reactive, meaning issues like model drift and computational inefficiencies are often identified only after significant performance degradation has occurred.

[0004] What differentiates the AI Model Control Framework for Optimized Performance from existing options is its ability to proactively monitor and adjust AI models in real time using adaptive algorithms. This system not only detects model drift but also optimizes computational resource usage, dynamically retrains models, and fine-tunes parameters without manual intervention. The novelty of this invention lies in its automated real-time monitoring, adaptive control mechanisms, and resource optimization features, which enhance model performance while reducing operational costs. By continuously learning and improving through feedback loops, the framework provides a scalable, sustainable solution that ensures AI systems operate at their best, regardless of changing data patterns or environmental conditions.

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to optimize AI model performance by providing real-time monitoring and control mechanisms that dynamically adjust model parameters.

[0006] Another object of the invention is to address the problem of model drift by implementing adaptive algorithms that detect and correct shifts in data distribution automatically.

[0007] Another object of the invention is to improve the efficiency of AI models by reducing computational resource usage through advanced resource optimization techniques.

[0008] Another object of the invention is to enhance the scalability of AI systems, allowing organizations to manage multiple AI models simultaneously without degradation in performance.
[0009] Another object of the invention is to provide a user-friendly interface that allows real-time monitoring and management of AI model performance metrics, enhancing user engagement and decision-making.

[00010] Another object of the invention is to minimize manual intervention by automating the retraining and fine-tuning processes, reducing operational costs and improving system reliability.

[00011] Another object of the invention is to support seamless integration with existing AI infrastructures, promoting interoperability and reducing barriers to adoption.

[00012] Another object of the invention is to enhance the security and reliability of AI systems by incorporating real-time anomaly detection and automated rollback mechanisms.

[00013] Another object of the invention is to contribute to sustainable development by reducing energy consumption and operational costs associated with AI model training and inference.

[00014] Another object of the invention is to provide a versatile solution applicable across various industries, including healthcare, finance, and autonomous systems, where real-time AI optimization is critical for operational success

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, AI model control framework for optimized performance in AI systems is presented. It provides an AI model control framework designed to optimize the performance of AI models through real-time monitoring, adaptive algorithms, and automated control mechanisms. It addresses key challenges such as model drift, computational inefficiencies, and suboptimal performance by dynamically adjusting model parameters. The framework enhances scalability, efficiency, and reliability, ensuring that AI systems operate effectively in changing environments. With its ability to reduce resource consumption and improve system performance, the invention is applicable across industries like healthcare, finance, and autonomous systems.

[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 AI model control framework for optimized performance in AI systems.

[00021] FIG 2 is working methodology of AI model control framework for optimized performance in AI systems.

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 AI model control framework for optimized performance in AI systems 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, AI model control framework for optimized performance in AI systems 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of monitoring module 102, evaluation engine 104, adaptation mechanism 106, feedback loop 108, real-time drift detection system 110, resource optimization module 112, user interface 114, integration layer 116, security mechanisms 118, scalability architecture 120, cloud and edge computing support 122.

[00029] Referring to Fig. 1, the present disclosure provides details of AI model control framework for optimized performance in AI systems 100 which is designed to enhance the performance of AI models using real-time monitoring, adaptive algorithms, and dynamic control mechanisms. The framework includes several key components such as the monitoring module 102, evaluation engine 104, and adaptation mechanism 106, which work together to track and improve model performance. The feedback loop 108 ensures continuous learning, while the real-time drift detection system 110 identifies shifts in data distribution. Additionally, the resource optimization module 112 reduces computational load, and the user interface 114 allows for real-time visualization and control. Further components like the integration layer 116 and cloud and edge computing support 122 ensure seamless deployment and scalability across platforms.

[00030] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with monitoring module 102, which continuously tracks and analyzes performance metrics like accuracy, resource usage, and precision. This component operates in real time, flagging any deviations from expected behavior, and provides essential data to the evaluation engine 104 for deeper analysis. The monitoring module 102 plays a critical role in detecting early signs of model drift and resource inefficiencies, ensuring other components can act promptly.

[00031] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with evaluation engine 104, which employs machine learning techniques and statistical tests to analyze model performance. It detects patterns indicating drift or inefficiencies, working closely with the monitoring module 102 to receive continuous updates on model behavior. Upon detecting any issues, the evaluation engine 104 activates the adaptation mechanism 106 to make necessary adjustments to the AI model.

[00032] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with adaptation mechanism 106, responsible for dynamically adjusting model parameters based on inputs from the evaluation engine 104. This component utilizes techniques like retraining, hyperparameter tuning, or architecture modification to improve performance. The adaptation mechanism 106 collaborates with the feedback loop 108, ensuring that any adjustments made are recorded and used to enhance future decision-making.

[00033] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with feedback loop 108, which captures the results of any adjustments made by the adaptation mechanism 106 and feeds this information back into the system. This ensures continuous learning and improvement by providing real-time feedback to the monitoring module 102 and evaluation engine 104, allowing them to refine future performance optimizations.

[00034] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with real-time drift detection system 110, which automatically detects shifts in data distribution. It integrates with the monitoring module 102 to monitor changes in data patterns and works closely with the evaluation engine 104 to assess the significance of the drift. If a drift is detected, the real-time drift detection system 110 triggers the adaptation mechanism 106 to initiate corrective actions, ensuring that the model remains accurate and effective

[00035] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with resource optimization module 112, which is responsible for minimizing the computational resources required during AI model training and inference. It employs techniques such as model pruning and quantization to enhance efficiency without compromising performance. The resource optimization module 112 works closely with the monitoring module 102, ensuring that computational usage remains within defined limits while maintaining optimal performance. This component contributes significantly to reducing energy consumption and operational costs.

[00036] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with user interface 114, which allows users to monitor AI model performance, visualize key metrics, and initiate manual interventions if necessary. This component offers real-time data on model health, performance, and resource utilization, giving users the ability to respond quickly to any issues. The user interface 114 interacts with the monitoring module 102 and evaluation engine 104 to provide accurate, up-to-date information to the user and facilitate decision-making.

[00037] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with integration layer 116, which ensures seamless compatibility and interoperability with existing AI infrastructures and systems. This component acts as a bridge between the framework and various AI models, data processing systems, and computational environments. The integration layer 116 works alongside the monitoring module 102 and resource optimization module 112 to ensure that the framework can be easily implemented across different platforms, including both cloud and edge computing environments.

[00038] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with security mechanisms 118, which safeguard the AI models from adversarial attacks and data poisoning. These mechanisms continuously monitor for any unusual activities or data manipulation attempts and ensure the integrity of the system. The security mechanisms 118 are tightly integrated with the monitoring module 102, which helps detect anomalies, and the feedback loop 108, which allows for automated rollback in case of a security breach.

[00039] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with scalability architecture 120, designed to support the simultaneous management and optimization of multiple AI models without performance degradation. This component ensures that the framework can scale to accommodate large, complex AI systems, particularly in environments where numerous models need to operate concurrently. The scalability architecture 120 works closely with the resource optimization module 112 to maintain efficient performance across all models while minimizing resource consumption.

[00040] Referring to Fig. 1, the AI Model Control Framework for Optimized Performance in AI Systems 100 is provided with cloud and edge computing support 122, which allows the framework to be deployed across various computational environments. This component enables real-time processing, regardless of whether the system is implemented in a centralized cloud environment or distributed edge devices. The cloud and edge computing support 122 ensures that the framework remains flexible and adaptable, working in conjunction with the integration layer 116 to enable seamless deployment across platforms

[00041] Referring to Fig 2, there is illustrated method 200 for AI Model Control Framework for Optimized Performance in AI Systems 100. The method comprises:

At step 202, method 200 includes the monitoring module 102 continuously tracking performance metrics such as accuracy, precision, and resource usage of the AI model;

At step 204, method 200 includes the evaluation engine 104 analyzing the data from the monitoring module 102 to detect any model drift or inefficiencies;

At step 206, method 200 includes the real-time drift detection system 110 identifying shifts in data distribution and flagging potential issues for adjustment;

At step 208, method 200 includes the adaptation mechanism 106 dynamically adjusting model parameters, such as retraining or fine-tuning, based on inputs from the evaluation engine 104;

At step 210, method 200 includes the feedback loop 108 capturing the outcomes of the adjustments made by the adaptation mechanism 106 and feeding this data back into the system for continuous learning;

At step 212, method 200 includes the resource optimization module 112 reducing computational resources by implementing techniques like model pruning and quantization during the retraining process;

At step 214, method 200 includes the user interface 114 displaying real-time performance data, alerting the user of any model issues, and allowing manual interventions if needed;

At step 216, method 200 includes the integration layer 116 ensuring smooth operation of the framework with existing AI infrastructures, whether deployed in cloud-based or edge computing environments.


[00042] 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.

[00043] 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.

[00044] 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. An AI Model Control Framework for Optimized Performance in AI Systems 100 comprising of
monitoring module 102 to continuously track AI model performance metrics;
evaluation engine 104 to analyze performance data and detect model drift;
adaptation mechanism 106 to dynamically adjust model parameters based on evaluation;
feedback loop 108 to capture and feed outcomes back into the system for continuous improvement;
real-time drift detection system 110 to identify shifts in data distribution;
resource optimization module 112 to reduce computational resource usage during training and inference;
user interface 114 to display real-time performance metrics and provide user control;
integration layer 116 to ensure compatibility with existing AI systems;
security mechanisms 118 to protect AI models from adversarial attacks and data poisoning; and
scalability architecture 120 to manage multiple AI models concurrently without performance degradation;
cloud and edge computing support 122 to allow deployment across cloud and edge environments

2. The AI Model Control Framework for Optimized Performance in AI Systems as claimed, wherein monitoring module 102 is configured to continuously track performance metrics, including accuracy, precision, recall, and resource usage, providing real-time insights into model behavior.
3. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein evaluation engine 104 is configured to analyze performance data, detect model drift, and identify inefficiencies by utilizing statistical methods and machine learning algorithms.

4. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein adaptation mechanism 106 is configured to dynamically adjust AI model parameters, including retraining, fine-tuning hyperparameters, or modifying architecture, based on insights provided by the evaluation engine.

5. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein feedback loop 108 is configured to capture outcomes of adjustments made by the adaptation mechanism and feed the results back into the system to enable continuous learning and optimization.

6. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein real-time drift detection system 110 is configured to automatically detect shifts in data distribution, flagging potential performance degradation and triggering corrective actions by the adaptation mechanism.

7. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein resource optimization module 112 is configured to reduce computational resource usage during model training and inference through techniques such as model pruning and quantization, thereby enhancing efficiency.

8. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein user interface 114 is configured to display real-time performance metrics, alert users of potential issues, and provide manual control options for initiating interventions and adjustments.

9. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein integration layer 116 is configured to ensure seamless interoperability with existing AI infrastructures and data processing environments, supporting cloud-based and edge computing deployments.

10. The AI Model Control Framework for Optimized Performance in AI Systems as claimed in claim 1, wherein method comprises of
monitoring module 102 continuously tracking performance metrics such as accuracy, precision, and resource usage of the AI model;
evaluation engine 104 analyzing the data from the monitoring module 102 to detect any model drift or inefficiencies;
real-time drift detection system 110 identifying shifts in data distribution and flagging potential issues for adjustment;
adaptation mechanism 106 dynamically adjusting model parameters, such as retraining or fine-tuning, based on inputs from the evaluation engine 104;
feedback loop 108 capturing the outcomes of the adjustments made by the adaptation mechanism 106 and feeding this data back into the system for continuous learning;
resource optimization module 112 reducing computational resources by implementing techniques like model pruning and quantization during the retraining process;
user interface 114 displaying real-time performance data, alerting the user of any model issues, and allowing manual interventions if needed;
integration layer 116 ensuring smooth operation of the framework with existing AI infrastructures, whether deployed in cloud-based or edge computing environments.

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