OPTIMIZATION SYSTEM FOR AI CALL IDENTIFICATION AND PERFORMANCE

Abstract

ABSTRACT Optimization System for AI Call Identification and Performance The present disclosure introduces an optimization system for AI call identification and performance 100 designed to enhance the efficiency and resource management of AI/ML pipelines. The system comprises of adaptive data preprocessing module 102 for dynamic data cleaning and augmentation and smart data sampling system 104 to reduce dataset size while preserving data quality. It employs a parallelized model training system 110 to allocate resources, complemented by a gradient accumulation and reduced precision training unit 112 for memory optimization. Hyperparameter optimization module 116 uses Bayesian and genetic algorithms to tune parameters, while the context-aware model pruning and quantization unit 120 reduces model complexity. An edge and cloud deployment optimization engine 122 adapts configurations based on environmental needs, and a real-time feedback loop mechanism 124 continuously monitors performance, adjusting pipeline parameters to maintain optimal performance across diverse deployment environments. Reference Fig 1

Specification

Description: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 optimisation system for AI call identification and performance 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, optimisation system for AI call identification and performance 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of adaptive data preprocessing module 102, smart data sampling system 104, on-the-fly data augmentation engine 106, incremental data preprocessing mechanism 108, parallelized model training system 110, gradient accumulation and reduced precision training unit 112, dynamic learning rate scheduler 114, hyperparameter optimization module 116, energy-aware resource scheduler 118, context-aware model pruning and quantization unit 120, edge and cloud deployment optimization engine 122, real-time feedback loop mechanism 124, self-tuning feedback loop 126, energy-efficient inference and batch processing module 128, green AI integration framework 130, adaptive resource allocation controller 132, real-time scalability module for AI/ML pipelines 134, cross-domain transfer learning-based hyperparameter tuning module 136, dependency-aware pipeline scheduling system 138, automated model deployment optimization engine 140, multi-model parallel processing system 142, energy-cost optimization module 144, adaptive model retraining scheduler 146 and edge-to-cloud federated learning optimization system 148.

[00029] Referring to Fig. 1, the present disclosure provides details optimisation system for AI call identification and performance 100. It is a comprehensive framework designed to enhance the efficiency, scalability, and energy utilization of AI/ML pipelines by optimizing each pipeline stage, from data preprocessing to model deployment. The optimization system for AI call identification and performance 100 includes key components such as adaptive data preprocessing module 102, smart data sampling system 104, and incremental data preprocessing mechanism 108, each facilitating reduced resource consumption and improved processing speed. Additionally, parallelized model training system 110 and gradient accumulation and reduced precision training unit 112 enable efficient training with minimized computational load. The system further incorporates dynamic learning rate scheduler 114 and hyperparameter optimization module 116 to streamline model accuracy and performance. Advanced components, such as energy-aware resource scheduler 118 and context-aware model pruning and quantization unit 120, support sustainable AI operations, while edge and cloud deployment optimization engine 122 enables adaptable deployment across various environments.

[00030] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with adaptive data preprocessing module 102, which dynamically adjusts data cleaning, transformation, and augmentation based on input characteristics. This module reduces unnecessary processing load, setting an efficient foundation for downstream tasks such as the smart data sampling system 104 and incremental data preprocessing mechanism 108. It works closely with the smart data sampling system 104 to ensure data quality and relevance, minimizing redundancy and optimizing data flow into the pipeline.

[00031] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with smart data sampling system 104, which selects representative data subsets, reducing the overall data size without losing critical information. This system enhances the efficiency of the adaptive data preprocessing module 102 by identifying essential data points, enabling faster processing and training. It collaborates with the on-the-fly data augmentation engine 106 to support synthetic data generation as needed, ensuring diverse yet manageable datasets.

[00032] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with on-the-fly data augmentation engine 106, which generates synthetic data points during training only as required. By preventing unnecessary storage of augmented data, it reduces the preprocessing burden on incremental data preprocessing mechanism 108 and supports real-time adaptation in streaming data applications. This engine interworks seamlessly with the smart data sampling system 104 to ensure data diversity and quality.

[00033] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with incremental data preprocessing mechanism 108, designed for efficient handling of real-time data through batch processing or streaming. It enables large-scale data flows without latency issues, working with adaptive data preprocessing module 102 to manage incoming data while optimizing resource use. This mechanism supports adaptive scalability, preparing data for immediate processing in the parallelized model training system 110.

[00034] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with parallelized model training system 110, which distributes training tasks across multiple CPUs, GPUs, or TPUs. It dynamically allocates resources to manage workload, reducing bottlenecks and enabling faster model training. This system is closely integrated with gradient accumulation and reduced precision training unit 112 to further enhance training efficiency by balancing computational precision and resource allocation.

[00035] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with gradient accumulation and reduced precision training unit 112, which enables large-batch training by accumulating gradients over multiple iterations. This approach optimizes memory usage and allows for training with reduced precision without significant loss in model accuracy. The unit works closely with the parallelized model training system 110 to maximize training efficiency while minimizing computational load, ensuring faster and more resource-efficient training cycles.

[00036] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with dynamic learning rate scheduler 114, which automatically adjusts the learning rate based on model performance during training. This scheduler reduces the number of training epochs needed, preventing issues such as over fitting or convergence on local minima. It collaborates with the hyperparameter optimization module 116 to fine-tune model parameters efficiently, adapting dynamically to the specific needs of each training phase.

[00037] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with hyperparameter optimization module 116, which uses a combination of Bayesian optimization and genetic algorithms to efficiently search for optimal hyperparameter settings. This module minimizes the computational load associated with traditional grid and random search methods, working alongside the dynamic learning rate scheduler 114 to improve model accuracy and performance with fewer iterations.

[00038] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with energy-aware resource scheduler 118, which dynamically allocates resources based on energy consumption profiles, ensuring sustainable operation of the pipeline. It reduces power consumption by prioritizing energy-efficient processors for less demanding tasks, and collaborates with context-aware model pruning and quantization unit 120 to optimize model complexity based on deployment needs, supporting green AI practices.

[00039] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with context-aware model pruning and quantization unit 120, which optimizes model size by removing non-essential parameters and reducing precision where possible. This unit balances model accuracy with performance, particularly in edge or latency-sensitive environments. It integrates seamlessly with the edge and cloud deployment optimization engine 122 to ensure efficient and accurate deployment across various platforms.

[00040] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with edge and cloud deployment optimization engine 122, which selects optimal hardware and software configurations for model deployment based on network conditions, available resources, and application requirements. This engine adapts deployment strategies dynamically, working with the real-time feedback loop mechanism 124 to monitor and adjust configurations, maximizing performance in both cloud and edge environments.

[00041] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with real-time feedback loop mechanism 124, which monitors performance metrics throughout the pipeline and adjusts parameters such as batch size and learning rate accordingly. This feedback loop optimizes each pipeline stage continuously, working with the self-tuning feedback loop 126 to maintain efficiency without manual intervention, enhancing overall system adaptability.

[00042] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with self-tuning feedback loop 126, which enables automatic adjustments in pipeline configurations by learning from past performance data. It enhances computational efficiency by reducing the need for manual fine-tuning, while working closely with the energy-efficient inference and batch processing module 128 to balance throughput and latency in high-demand applications.

[00043] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with energy-efficient inference and batch processing module 128, which supports batch processing for high-throughput tasks and asynchronous processing for latency-sensitive applications. This module minimizes resource consumption during inference, coordinating with the green AI integration framework 130 to track and report on energy usage, promoting sustainable AI operations.

[00044] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with green AI integration framework 130, which monitors the carbon footprint of AI operations by integrating with external green AI frameworks. This framework provides real-time feedback on energy consumption, enabling users to make informed decisions about resource allocation, and works with the adaptive resource allocation controller 132 to adjust power usage based on environmental impact goals.

[00045] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with adaptive resource allocation controller 132, which dynamically reallocates computational resources across pipeline stages based on workload demands. It ensures that high-computation tasks receive necessary resources while lower-priority tasks are assigned to energy-efficient processors, working in conjunction with the real-time scalability module for AI/ML pipelines 134 to maintain pipeline efficiency under varying load conditions.

[00046] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with real-time scalability module for AI/ML pipelines 134, designed to handle dynamic AI workloads by scaling computational resources elastically based on real-time demand. This module ensures responsiveness during workload spikes, coordinating with cross-domain transfer learning-based hyperparameter tuning module 136 to optimize performance across diverse application domains.

[00047] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with cross-domain transfer learning-based hyperparameter tuning module 136, which expedites hyperparameter optimization by leveraging prior knowledge from models trained on similar tasks. It reduces tuning time and resource use in new scenarios, while working with dependency-aware pipeline scheduling system 138 to adjust pipeline tasks efficiently based on inter-stage dependencies.

[00048] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with dependency-aware pipeline scheduling system 138, which optimizes task execution order and concurrency based on inter-stage dependencies to improve overall pipeline efficiency. It interacts with the automated model deployment optimization engine 140 to streamline deployment, ensuring that resource-intensive tasks are prioritized for optimal performance.

[00049] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with automated model deployment optimization engine 140, which dynamically selects deployment strategies-whether edge, cloud, or hybrid-based on factors like network bandwidth and latency requirements. It enables seamless switching between strategies in real-time, working with the multi-model parallel processing system 142 to maximize efficiency across multiple model deployments.

[00050] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with multi-model parallel processing system 142, which allows concurrent processing of multiple models on shared hardware infrastructure. This system allocates resources based on task criticality, working with energy-cost optimization module 144 to ensure balanced resource use, minimizing costs and maximizing throughput without compromising performance.

[00051] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with energy-cost optimization module 144, which balances the pipeline between cloud and on-premises environments based on real-time cost-benefit analysis, energy availability, and performance requirements. It works with the adaptive model retraining scheduler 146 to allocate resources cost-effectively, especially during model retraining cycles.

[00052] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with adaptive model retraining scheduler 146, which automatically triggers model retraining based on factors such as performance degradation or data drift. This scheduler balances retraining frequency with resource availability, coordinating with edge-to-cloud federated learning optimization system 148 to ensure retraining is efficient and well-timed across distributed environments.

[00053] Referring to Fig. 1, optimization system for AI call identification and performance 100 is provided with edge-to-cloud federated learning optimization system 148, which distributes computation between edge devices and cloud servers for federated learning tasks. It optimizes resource allocation based on latency and energy constraints, interacting with the other pipeline components to ensure efficient training and adaptation across a federated network.

[00054] Referring to Fig 2, there is illustrated method 200 for optimization system for AI call identification and performance 100. The method comprises:

At step 202, method 200 includes adaptive data preprocessing module 102 analyzing incoming raw data and performing essential cleaning, transformation, and augmentation to prepare data for subsequent processing;

At step 204, method 200 includes smart data sampling system 104 selecting representative subsets from the preprocessed data, reducing dataset size while retaining key characteristics, and forwarding optimized data to the next stage;

At step 206, method 200 includes on-the-fly data augmentation engine 106 generating synthetic data points only when required during training, adding diversity to the dataset without unnecessary storage and optimizing data for the training phase;

At step 208, method 200 includes incremental data preprocessing mechanism 108 processing data in batches or as it arrives, ensuring low-latency handling for large-scale or real-time applications and transferring prepared data to the model training stage;

At step 210, method 200 includes parallelized model training system 110 distributing training tasks across multiple processors (CPUs, GPUs, or TPUs), utilizing the optimized dataset and dynamically allocating resources to speed up training;

At step 212, method 200 includes gradient accumulation and reduced precision training unit 112 accumulating gradients over multiple training iterations to enable large-batch training and applying reduced precision calculations, improving memory efficiency while retaining model accuracy;

At step 214, method 200 includes dynamic learning rate scheduler 114 adjusting the learning rate in real-time based on model performance during training, reducing the risk of overfitting or converging prematurely and preparing optimized weights for the model;

At step 216, method 200 includes hyperparameter optimization module 116 using Bayesian optimization and genetic algorithms to identify optimal hyperparameter configurations, feeding these configurations back into the training cycle to improve model performance;

At step 218, method 200 includes energy-aware resource scheduler 118 monitoring energy consumption across the system and allocating resources based on energy profiles, ensuring an energy-efficient approach for all pipeline stages;

At step 220, method 200 includes context-aware model pruning and quantization unit 120 reducing model size and complexity by pruning non-essential parameters, which is crucial for preparing models for efficient inference on resource-constrained environments;

At step 222, method 200 includes edge and cloud deployment optimization engine 122 selecting the appropriate deployment strategy (edge, cloud, or hybrid) based on model size and latency requirements, configuring the model for efficient deployment;

At step 224, method 200 includes real-time feedback loop mechanism 124 monitoring pipeline performance metrics (e.g., latency, accuracy) and adjusting parameters such as batch size or hardware usage as needed, optimizing performance dynamically;

At step 226, method 200 includes self-tuning feedback loop 126 using historical data to automatically refine pipeline configurations, maintaining peak efficiency and continuously adapting based on feedback from the real-time feedback loop mechanism 124;

At step 228, method 200 includes energy-efficient inference and batch processing module 128 executing inference tasks in batch or asynchronously, depending on application demands, reducing latency for real-time applications while minimizing energy usage;

At step 230, method 200 includes green AI integration framework 130 tracking and reporting real-time energy consumption and carbon footprint of the AI/ML operations, allowing users to make sustainability-focused adjustments;

At step 232, method 200 includes adaptive resource allocation controller 132 reallocating resources across pipeline stages to ensure that high-demand tasks receive priority while optimizing energy use for lower-priority tasks;

At step 234, method 200 includes real-time scalability module for AI/ML pipelines 134 enabling elastic scaling to accommodate shifts in workload intensity, ensuring responsive resource allocation during peak processing demands;

At step 236, method 200 includes cross-domain transfer learning-based hyperparameter tuning module 136 leveraging knowledge from previously optimized models to adjust hyperparameters quickly in similar tasks, accelerating tuning in new scenarios;

At step 238, method 200 includes dependency-aware pipeline scheduling system 138 adjusting execution order and task concurrency based on inter-stage dependencies, creating an optimized workflow across the pipeline;

At step 240, method 200 includes automated model deployment optimization engine 140 selecting the most suitable deployment configuration (e.g., edge-based or cloud-based) in real time, ensuring the model performs optimally in its specific deployment environment;

At step 242, method 200 includes multi-model parallel processing system 142 managing concurrent processing of multiple models on shared hardware, prioritizing critical models to ensure efficient resource utilization and task completion;

At step 244, method 200 includes energy-cost optimization module 144 balancing the resource load between cloud and on-premises environments, minimizing both energy consumption and operational costs based on workload demands;

At step 246, method 200 includes adaptive model retraining scheduler 146 triggering retraining cycles only when significant performance degradation or data drift is detected, optimizing retraining frequency based on real-time resource availability;

At step 248, method 200 includes edge-to-cloud federated learning optimization system 148 distributing learning tasks across edge devices and cloud infrastructure based on latency requirements, energy profiles, and network availability, ensuring efficient and distributed learning across environments.


[00055] 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.

[00056] 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.

[00057] 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 optimization system for AI call identification and performance 100 comprising of
adaptive data preprocessing module 102 to analyze and prepare incoming data for optimized processing;
smart data sampling system 104 to select representative data subsets and reduce dataset size;
on-the-fly data augmentation engine 106 to generate synthetic data during training only when needed;
incremental data preprocessing mechanism 108 to process data in batches or as it arrives for low-latency handling; parallelized model training system 110 to distribute training tasks across multiple processors for faster training;
gradient accumulation and reduced precision training unit 112 to optimize memory and computation during large-batch training; dynamic learning rate scheduler 114 to adjust learning rate in real-time to enhance model performance; hyperparameter optimization module 116 to efficiently search for optimal hyperparameters using advanced algorithms;
energy-aware resource scheduler 118 to allocate resources based on energy profiles for efficiency;
context-aware model pruning and quantization unit 120 to reduce model size and complexity for efficient deployment; edge and cloud deployment optimization engine 122 to determine optimal deployment strategy based on network and resource conditions;
real-time feedback loop mechanism 124 to monitor performance metrics and adjust pipeline parameters dynamically;
self-tuning feedback loop 126 to continuously refine pipeline configurations using historical data;
energy-efficient inference and batch processing module 128 to execute inference tasks with minimal latency and energy use;
green AI integration framework 130 to track and report energy consumption for sustainability;
adaptive resource allocation controller 132 to dynamically allocate resources across pipeline stages;
real-time scalability module for AI/ML pipelines 134 to enable elastic scaling to handle workload shifts;
cross-domain transfer learning-based hyperparameter tuning module 136 to leverage prior knowledge for efficient tuning in similar tasks;
dependency-aware pipeline scheduling system 138 to optimize task order and concurrency based on inter-stage dependencies;
automated model deployment optimization engine 140 to select the best deployment configuration in real-time;
multi-model parallel processing system 142 to manage concurrent model processing on shared hardware;
energy-cost optimization module 144 to balance resource load for cost and energy savings;
adaptive model retraining scheduler 146 to trigger retraining based on performance and data drift; and
edge-to-cloud federated learning optimization system 148 to distribute learning tasks across edge and cloud for efficient, distributed learning.

2. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein adaptive data preprocessing module 102 is configured to dynamically adjust data cleaning, transformation, and augmentation based on input data characteristics, enabling efficient processing and reducing resource load across downstream stages.

3. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein smart data sampling system 104 is configured to select representative data subsets, preserving critical data attributes while minimizing data volume, thereby accelerating processing speed and enhancing data relevancy for subsequent stages.

4. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein parallelized model training system 110 is configured to distribute training tasks across multiple processors, dynamically allocating resources based on model complexity and available hardware, facilitating faster and more resource-efficient model training.

5. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein gradient accumulation and reduced precision training unit 112 is configured to perform gradient accumulation over iterations and apply reduced precision calculations selectively, optimizing memory and computational efficiency without compromising model accuracy.

6. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein hyperparameter optimization module 116 is configured to employ Bayesian optimization and genetic algorithms, reducing the computational cost of hyperparameter tuning while maximizing model accuracy and performance.

7. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein context-aware model pruning and quantization unit 120 is configured to adaptively prune non-essential model parameters and apply quantization based on deployment context, optimizing model size and complexity for improved inference performance.

8. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein edge and cloud deployment optimization engine 122 is configured to select the optimal deployment strategy, including edge, cloud, or hybrid configurations, based on resource availability, latency requirements, and environmental conditions, ensuring efficient and adaptable deployment.

9. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein real-time feedback loop mechanism 124 is configured to monitor pipeline performance metrics continuously, dynamically adjusting parameters such as batch size, learning rate, and resource allocation to maintain optimal performance and responsiveness across all pipeline stages.

10. The optimization system for AI call identification and performance 100 as claimed in claim 1, wherein method comprises of
adaptive data preprocessing module 102 analyzing incoming raw data and performing essential cleaning, transformation, and augmentation to prepare data for subsequent processing;

smart data sampling system 104 selecting representative subsets from the preprocessed data, reducing dataset size while retaining key characteristics, and forwarding optimized data to the next stage;

on-the-fly data augmentation engine 106 generating synthetic data points only when required during training, adding diversity to the dataset without unnecessary storage and optimizing data for the training phase;

incremental data preprocessing mechanism 108 processing data in batches or as it arrives, ensuring low-latency handling for large-scale or real-time applications and transferring prepared data to the model training stage;

parallelized model training system 110 distributing training tasks across multiple processors (CPUs, GPUs, or TPUs), utilizing the optimized dataset and dynamically allocating resources to speed up training;

gradient accumulation and reduced precision training unit 112 managing large-batch training using gradient accumulation and reduced precision calculations to optimize memory and computational efficiency while retaining model accuracy;

dynamic learning rate scheduler 114 adjusting the learning rate in real-time based on model performance during training, reducing the risk of over fitting or converging prematurely and preparing optimized weights for the model;

hyperparameter optimization module 116 using Bayesian optimization and genetic algorithms to identify optimal hyperparameter configurations, feeding these configurations back into the training cycle to improve model performance;

energy-aware resource scheduler 118 monitoring energy consumption across the system and allocating resources based on energy profiles, ensuring an energy-efficient approach for all pipeline stages;

context-aware model pruning and quantization unit 120 reducing model size and complexity by pruning non-essential parameters, crucial for preparing models for efficient inference on resource-constrained environments;

edge and cloud deployment optimization engine 122 selecting the appropriate deployment strategy (edge, cloud, or hybrid) based on model size and latency requirements, configuring the model for efficient deployment;

real-time feedback loop mechanism 124 monitoring performance metrics across the pipeline and adjusting parameters like batch size and resource allocation to enhance overall system efficiency;

self-tuning feedback loop 126 using historical data to automatically refine pipeline configurations, maintaining peak efficiency and continuously adapting based on feedback from the real-time feedback loop mechanism 124;

energy-efficient inference and batch processing module 128 executing inference tasks in batch or asynchronously, depending on application demands, reducing latency for real-time applications while minimizing energy usage;

green AI integration framework 130 tracking and reporting real-time energy consumption and carbon footprint of the AI/ML operations, allowing users to make sustainability-focused adjustments;

adaptive resource allocation controller 132 reallocating resources across pipeline stages to ensure that high-demand tasks receive priority while optimizing energy use for lower-priority tasks;

real-time scalability module for AI/ML pipelines 134 enabling elastic scaling to accommodate shifts in workload intensity, ensuring responsive resource allocation during peak processing demands;

cross-domain transfer learning-based hyperparameter tuning module 136 leveraging knowledge from previously optimized models to adjust hyperparameters quickly in similar tasks, accelerating tuning in new scenarios;

dependency-aware pipeline scheduling system 138 adjusting execution order and task concurrency based on inter-stage dependencies, creating an optimized workflow across the pipeline;

automated model deployment optimization engine 140 selecting the most suitable deployment configuration (e.g., edge-based or cloud-based) in real time, ensuring the model performs optimally in its specific deployment environment;

multi-model parallel processing system 142 managing concurrent processing of multiple models on shared hardware, prioritizing critical models to ensure efficient resource utilization and task completion;

energy-cost optimization module 144 balancing the resource load between cloud and on-premises environments, minimizing both energy consumption and operational costs based on workload demands;

adaptive model retraining scheduler 146 triggering retraining cycles only when significant performance degradation or data drift is detected, optimizing retraining frequency based on real-time resource availability;

edge-to-cloud federated learning optimization system 148 distributing learning tasks across edge devices and cloud infrastructure based on latency requirements, energy profiles, and network availability, ensuring efficient and distributed learning across environments.

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