AI IMPLEMENTATION ACCELERATION SYSTEM WITH MODEL SETTING METHODOLOGY

Abstract

ABSTRACT AI Implementation Acceleration System with Model Setting Methodology The present disclosure introduces AI Implementation Acceleration System with Model Setting Methodology 100, which streamlines the deployment and optimization of AI models. It comprise of data ingestion module 102 for gathering and preprocessing data, and model repository 104 for storing pre-trained models. Users interact with model selection interface 106 to choose suitable models, aided by the dynamic model recommendation engine 114. The configuration and tuning module 108 automates model fine-tuning, while the deployment engine 110 handles seamless integration into on-premises, cloud, or hybrid environments. Real-time performance is monitored by feedback system 112, with insights visualized via the real-time monitoring dashboard 118. Its other components are feedback-driven iterative improvement system 120, multi-deployment compatibility framework 122, stakeholder collaboration tools 124, contextual data preprocessing module 126, user-centric model customization interface 128, knowledge base integration 130, scalability through modular architecture 132, compliance and ethical AI features 134, scenario-based simulation environment 136 . Reference Fig 1

Specification

Description:AI Implementation Acceleration System with Model Setting Methodology
TECHNICAL FIELD
[0001] The present innovation relates to systems and methodologies for accelerating the deployment and optimization of artificial intelligence (AI) models across various industries.

BACKGROUND

[0002] The rapid adoption of artificial intelligence (AI) across industries has led to significant advancements in sectors such as healthcare, finance, manufacturing, and transportation. However, integrating AI models into business workflows presents substantial challenges. One of the primary difficulties lies in the complexity of model selection, tuning, and deployment. Organizations often struggle with determining the most suitable AI algorithms, adjusting model parameters for specific tasks, and ensuring optimal performance, which typically requires significant expertise and time. Traditional methods for AI implementation involve manually selecting algorithms, fine-tuning models, and performing numerous iterations to achieve optimal performance. These approaches are resource-intensive and can cause delays, increased costs, and missed business opportunities.

[0003] Existing solutions primarily offer fragmented approaches, focusing either on individual AI components like algorithm development or on infrastructure setup. However, these solutions often fail to address the entire lifecycle of AI implementation, leading to suboptimal model performance, slower deployment, and higher operational overhead. Additionally, many organizations without advanced AI expertise find these tools challenging to use, further complicating AI adoption.

[0004] The AI Implementation Acceleration System with Model Setting Methodology overcomes these drawbacks by providing an integrated framework that simplifies the entire AI implementation process. This invention distinguishes itself by offering automated model selection, hyperparameter tuning, real-time monitoring, and iterative improvement mechanisms. The system allows users to efficiently select, configure, and deploy AI models without deep technical knowledge, streamlining the process and reducing time to value. Its novel features include a dynamic recommendation engine, automated performance tuning, and cross-industry adaptability, all within a scalable, user-friendly interface. By addressing key challenges such as manual tuning and the lack of comprehensive AI tools, this invention significantly enhances AI adoption, enabling organizations to leverage the full potential of AI technologies more effectively.


OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to streamline the AI implementation process by providing an integrated system that automates model selection, configuration, and optimization.

[0006] Another object of the invention is to reduce the time and resources required for deploying AI models, making AI adoption more accessible to organizations with limited technical expertise.

[0007] Another object of the invention is to enhance the performance of AI models through automated hyperparameter tuning and real-time monitoring, ensuring optimal results with minimal manual intervention.

[0008] Another object of the invention is to provide a user-friendly interface that simplifies the AI model configuration process, enabling users to make adjustments without requiring in-depth knowledge of AI algorithms.
[0009] Another object of the invention is to support cross-industry adaptation by offering a framework that allows AI models to be easily customized and deployed across various sectors, including healthcare, finance, manufacturing, and transportation.

[00010] Another object of the invention is to improve scalability by offering a modular system architecture that can grow with an organization's AI needs, allowing seamless integration of new models, data sources, and features.


[00011] Another object of the invention is to ensure continuous model improvement by enabling iterative refinement based on user feedback and performance data, helping organizations maintain model accuracy and effectiveness over time.

[00012] Another object of the invention is to foster collaboration among stakeholders by incorporating tools that facilitate communication between data scientists, business analysts, and management during the AI implementation process.

[00013] Another object of the invention is to provide advanced resource optimization features that recommend the most efficient use of computational and data resources for AI model training and deployment.

[00014] Another object of the invention is to promote responsible AI usage by integrating compliance checks and ethical AI guidelines, ensuring that models adhere to regulatory standards and ethical considerations

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, AI implementation acceleration system with model setting methodology is presented. It streamlines the deployment, configuration, and optimization of AI models across industries. It automates model selection, hyperparameter tuning, and real-time monitoring, enabling faster AI adoption with minimal technical expertise. The system features a user-friendly interface, scalability, and cross-industry adaptability, promoting efficient resource use and continuous model refinement. It also incorporates ethical AI guidelines and compliance checks to ensure responsible AI implementation. Overall, the invention simplifies AI integration, improving performance and reducing time to value for organizations.

[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 implementation acceleration system with model setting methodology.

[00021] FIG 2 is working methodology of AI implementation acceleration system with model setting methodology.




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 implementation acceleration system with model setting methodology 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 implementation acceleration system with model setting methodology 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of data ingestion module 102, model repository 104, model selection interface 106, configuration and tuning module 108, deployment engine 110, monitoring and feedback system 112, dynamic model recommendation engine 114, automated hyperparameter tuning 116, real-time monitoring dashboard 118, feedback-driven iterative improvement system 120, multi-deployment compatibility framework 122, stakeholder collaboration tools 124, contextual data preprocessing module 126, user-centric model customization interface 128, knowledge base integration 130, scalability through modular architecture 132, compliance and ethical AI features 134 and scenario-based simulation environment 136.

[00029] Referring to Fig. 1, the present disclosure provides details of AI implementation acceleration system with model setting methodology 100. It is a comprehensive framework designed to streamline the deployment, optimization, and management of AI models across various industries. In one of the embodiments, the AI Implementation Acceleration System 100 may be provided with the following key components such as data ingestion module 102, model repository 104, and model selection interface 106, which facilitate data gathering, storage, and model selection. The system incorporates configuration and tuning module 108 and deployment engine 110 to automate model configuration and deployment. It also features monitoring and feedback system 112 and real-time monitoring dashboard 118 to track performance and make adjustments. Additional components such as scalability through modular architecture 132 and compliance and ethical AI features 134 ensure the system's adaptability and regulatory adherence.

[00030] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with data ingestion module 102, which gathers and preprocesses data from various sources, including databases, APIs, and IoT devices. It ensures the data is clean, structured, and ready for analysis. The data ingestion module 102 works closely with the contextual data preprocessing module 126 to tailor data cleaning techniques according to the nature of the data, optimizing it for model training in the model repository 104.

[00031] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with model repository 104, a centralized storage for pre-trained AI models that include performance metrics, use cases, and compatibility details. The model repository 104 interacts with the model selection interface 106 to allow users to browse and select the best models for their applications. This repository also shares data with the monitoring and feedback system 112 to track model performance post-deployment.

[00032] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with model selection interface 106, which helps users choose AI models suited to their operational needs. It offers guided recommendations, performance comparisons, and visual analytics for decision-making. The model selection interface 106 connects with the dynamic model recommendation engine 114, which uses historical performance data to provide optimized suggestions, ensuring the right model is selected efficiently.

[00033] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with configuration and tuning module 108, which automates the adjustment of AI model parameters to improve performance. Using optimization algorithms such as grid search and Bayesian methods, the configuration and tuning module 108 reduces the time needed for manual model tuning. It works in tandem with the automated hyperparameter tuning 116 to ensure continuous model optimization as new data and objectives arise.

[00034] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with deployment engine 110, which facilitates the seamless deployment of AI models in various environments, including on-premises, cloud, and hybrid setups. The deployment engine 110 ensures smooth integration with existing systems and communicates with the multi-deployment compatibility framework 122 to handle infrastructure flexibility. This component ensures the model's deployment process is streamlined and adaptable to user requirements.

[00035] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with monitoring and feedback system 112, which tracks AI model performance post-deployment. It collects real-time metrics such as accuracy and speed and integrates with the real-time monitoring dashboard 118 to provide visual insights to users. The monitoring and feedback system 112 ensures that performance declines or issues are detected early, allowing for adjustments through the feedback-driven iterative improvement system 120.

[00036] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with dynamic model recommendation engine 114, which uses machine learning algorithms to analyze historical performance data and recommend the most suitable AI models. The dynamic model recommendation engine 114 works closely with the model selection interface 106 to provide personalized suggestions, ensuring that the models align with the specific goals of the organization. It adapts to new data as it becomes available, making it a continuously learning system.

[00037] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with automated hyperparameter tuning 116, which uses reinforcement learning and evolutionary algorithms to automate the fine-tuning of AI models. This component integrates with the configuration and tuning module 108 to adjust model parameters without manual input, significantly reducing time and resources required for tuning. The automated hyperparameter tuning 116 ensures models perform optimally as they encounter new data or changes in operational objectives.

[00038] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with real-time monitoring dashboard 118, which visualizes key performance indicators (KPIs) such as model accuracy, speed, and resource usage. The real-time monitoring dashboard 118 works in tandem with the monitoring and feedback system 112 to display alerts for model drift or performance degradation, enabling users to act quickly to maintain model efficacy. This component is critical for continuous oversight and quick response to performance changes.

[00039] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with feedback-driven iterative improvement system 120, which enables continuous refinement of AI models based on real-time performance data and user feedback. This system works alongside the monitoring and feedback system 112 to gather performance insights and suggest model adjustments. By incorporating user feedback, the feedback-driven iterative improvement system 120 ensures that models remain aligned with evolving organizational needs.

[00040] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with multi-deployment compatibility framework 122, which supports the deployment of AI models across on-premises, cloud, and hybrid infrastructures. The multi-deployment compatibility framework 122 interacts with the deployment engine 110 to ensure seamless integration of models, regardless of the underlying infrastructure, enabling organizations to choose the most suitable deployment environment.

[00041] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with stakeholder collaboration tools 124, which facilitate communication among data scientists, business analysts, and management throughout the AI implementation process. These tools work in conjunction with the customizable reporting dashboard 118 to ensure that all stakeholders are informed about model performance and decision-making, fostering collaboration and alignment across teams.

[00042] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with contextual data preprocessing module 126, which automatically adjusts data cleaning and transformation techniques based on the type of data being ingested. It works closely with the data ingestion module 102 to ensure that data is properly formatted and optimized for model training, improving the overall quality of the input data used in AI models.

[00043] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with user-centric model customization interface 128, which allows non-technical users to customize AI models based on their specific requirements. The user-centric model customization interface 128 works in tandem with the model selection interface 106 to simplify the customization process, empowering users to adjust models without requiring deep technical expertise.

[00044] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with knowledge base integration 130, which includes best practices, case studies, and troubleshooting guides to assist users in optimizing their AI implementations. The knowledge base integration 130 interacts with the stakeholder collaboration tools 124 to provide resources and recommendations that help streamline the AI adoption process.

[00045] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with scalability through modular architecture 132, which allows organizations to scale their AI capabilities as their needs evolve. The scalability through modular architecture 132 works in conjunction with the deployment engine 110 and multi-deployment compatibility framework 122 to ensure the system can easily adapt to growing demands and new models.

[00046] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with compliance and ethical AI features 134, which ensure that AI models adhere to regulatory standards and ethical guidelines. These features work alongside the monitoring and feedback system 112 to assess the compliance of deployed models and to provide alerts or adjustments if models deviate from accepted practices.

[00047] Referring to Fig. 1, the AI Implementation Acceleration System with Model Setting Methodology 100 is provided with scenario-based simulation environment 136, which allows users to test AI models in hypothetical business scenarios before deploying them. The scenario-based simulation environment 136 works with the real-time monitoring dashboard 118 to simulate performance under various conditions, helping organizations assess potential risks and make informed deployment decisions.

[00048] Referring to Fig 2, there is illustrated method 200 for AI implementation acceleration system with model setting methodology 100. The method comprises:

At step 202, method 200 includes the system gathering data through the data ingestion module 102, where data is collected from various sources such as databases, APIs, or IoT devices based on input provided by the user;

At step 204, method 200 includes the system preprocessing the collected data using the contextual data preprocessing module 126, which cleans and structures the data according to the specific requirements of the selected AI models, ensuring it is ready for analysis;
At step 206, method 200 includes the user accessing the model selection interface 106 to browse and choose suitable pre-trained AI models stored in the model repository 104, with the dynamic model recommendation engine 114 providing suggestions based on the user's operational goals and historical performance data;

At step 208, method 200 includes the system configuring the selected AI model using the configuration and tuning module 108, where model parameters are automatically fine-tuned through optimization algorithms such as grid search or Bayesian optimization to achieve optimal performance;

At step 210, method 200 includes the system deploying the tuned AI model using the deployment engine 110, which ensures smooth integration of the model into the user's chosen environment, whether on-premises, cloud-based, or hybrid infrastructure;

At step 212, method 200 includes the system monitoring the real-time performance of the deployed AI model through the monitoring and feedback system 112, tracking key performance indicators (KPIs) such as model accuracy, speed, and resource utilization;

At step 214, method 200 includes the system visualizing performance metrics via the real-time monitoring dashboard 118, displaying critical insights and issuing alerts in case of model drift or performance degradation, allowing users to take immediate action;

At step 216, method 200 includes the user providing feedback, and the system applying adjustments to the AI model through the feedback-driven iterative improvement system 120, refining the model's configuration based on real-time performance data and user feedback, ensuring ongoing optimization and alignment with evolving objectives.


[00049] 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.

[00050] 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.

[00051] 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 implementation acceleration system with model setting methodology 100 comprising of
data ingestion module 102 to gather data from various sources, including databases, APIs, and IoT devices;
model repository 104 to store pre-trained AI models along with their performance metrics and use cases;
model selection interface 106 to enable users to browse and select suitable AI models;
configuration and tuning module 108 to automate the fine-tuning of AI model parameters for optimal performance;
deployment engine 110 to seamlessly deploy AI models into on-premises, cloud-based, or hybrid environments;
monitoring and feedback system 112 to track real-time performance of deployed AI models;
dynamic model recommendation engine 114 to provide suggestions for optimal AI models based on historical performance data;
automated hyperparameter tuning 116 to automatically adjust model parameters using advanced optimization techniques;
real-time monitoring dashboard 118 to visualize key performance indicators and model performance metrics;
feedback-driven iterative improvement system 120 to refine AI models based on performance data and user feedback;
multi-deployment compatibility framework 122 to support deployment across various infrastructures like on-premises, cloud, and hybrid;
stakeholder collaboration tools 124 to facilitate communication between data scientists, analysts, and management;
contextual data preprocessing module 126 to adjust data cleaning processes based on the specific data being ingested;
user-centric model customization interface 128 to allow non-technical users to customize AI models easily;
knowledge base integration 130 to provide access to best practices, case studies, and troubleshooting guides for AI implementations;
scalability through modular architecture 132 to enable seamless scaling of AI capabilities as organizational needs grow;
compliance and ethical AI features 134 to ensure that AI models adhere to regulatory standards and ethical guidelines;
scenario-based simulation environment 136 to test AI models in hypothetical business scenarios before deployment.

2. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein data ingestion module 102 is configured to gather and preprocess data from various sources such as databases, APIs, and IoT devices, ensuring the data is structured and ready for model training and analysis.

3. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein model repository 104 is configured to store pre-trained AI models, each accompanied by performance metrics, use cases, and compatibility details, enabling users to make informed model selections.

4. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein model selection interface 106 is configured to provide a user-friendly platform that allows users to browse and select suitable AI models, with recommendations offered by the dynamic model recommendation engine 114 based on historical performance data.

5. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein configuration and tuning module 108 is configured to automatically fine-tune the parameters of selected AI models using optimization techniques such as grid search and Bayesian optimization to enhance model performance.

6. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein deployment engine 110 is configured to deploy AI models into on-premises, cloud-based, or hybrid environments, ensuring seamless integration with existing systems and workflows.

7. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein monitoring and feedback system 112 is configured to track real-time performance metrics, including model accuracy and speed, and provide ongoing insights into model effectiveness post-deployment.

8. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein feedback-driven iterative improvement system 120 is configured to refine and adjust AI models based on real-time performance data and user feedback, ensuring continuous model optimization and alignment with business objectives.

9. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein scalability through modular architecture 132 is configured to enable the system to scale seamlessly, allowing organizations to expand their AI capabilities by integrating new models, data sources, and features as their needs evolve.


10. The AI Implementation Acceleration System with Model Setting Methodology 100 as claimed in claim 1, wherein method comprises of
data ingestion module 102 gathering data from various sources such as databases, apis, or iot devices based on input provided by the user;
contextual data preprocessing module 126 preprocessing the collected data, cleaning and structuring it according to the specific requirements of the selected ai models, ensuring it is ready for analysis;
model selection interface 106 being accessed by the user to browse and choose suitable pre-trained AI models stored in the model repository 104, with dynamic model recommendation engine 114 providing suggestions based on the user's operational goals and historical performance data;
configuration and tuning module 108 configuring the selected AI model, where model parameters are automatically fine-tuned through optimization algorithms such as grid search or bayesian optimization to achieve optimal performance;
deployment engine 110 deploying the tuned ai model, ensuring smooth integration of the model into the user's chosen environment, whether on-premises, cloud-based, or hybrid infrastructure;
monitoring and feedback system 112 monitoring the real-time performance of the deployed AI model, tracking key performance indicators (KPIS) such as model accuracy, speed, and resource utilization;
real-time monitoring dashboard 118 visualizing performance metrics, displaying critical insights, and issuing alerts in case of model drift or performance degradation, allowing users to take immediate action;
feedback-driven iterative improvement system 120 applying adjustments to the AI model, refining its configuration based on real-time performance data and user feedback, ensuring ongoing optimization and alignment with evolving objectives

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