PERFORMANCE OPTIMIZATION FRAMEWORK FOR AI AND MACHINE LEARNING PIPELINES

Abstract

ABSTRACT Performance Optimization Framework for AI and Machine Learning Pipelines The present disclosure introduces a performance optimization framework for AI and ML pipelines 100 designed to enhance efficiency, scalability, and sustainability across all pipeline stages. Key components are adaptive data preprocessing module 102 for dynamic data handling, model training optimization module 104 for efficient workload distribution, and hyperparameter optimization module 106 for rapid parameter tuning. Inference and deployment optimization module 108 reduces latency, while resource and energy efficiency module 110 manages real-time resource allocation to minimize power usage. Additional components are scalability and real-time processing module 112, self-optimizing feedback loop 114, context-aware data augmentation pipeline 116, cross-stage dependency optimization module 118 , automated model deployment optimization engine 120 ,energy and cost optimization module 122, adaptive model retraining scheduler 124, multi-model parallel processing engine 126, federated learning resource balancing module 128, and dynamic workload forecasting module 130 for predictive resource allocation. 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 performance optimisation framework for AI and machine learning pipeline 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, performance optimisation framework for AI and machine learning pipeline 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of adaptive data preprocessing module 102, model training optimization module 104, hyperparameter optimization module 106, inference and deployment optimization module 108, resource and energy efficiency module 110, scalability and real-time processing module 112, self-optimizing feedback loop 114, context-aware data augmentation pipeline 116, cross-stage dependency optimization module 118, automated model deployment optimization engine 120, energy and cost optimization module 122, adaptive model retraining scheduler 124, multi-model parallel processing engine 126, federated learning resource balancing module 128 and dynamic workload forecasting module 130.

[00029] Referring to Fig. 1, the present disclosure provides details of performance optimisation framework for AI and machine learning pipeline 100. It is designed to enhance efficiency, scalability, and sustainability across all stages of the pipeline by optimizing computational resources, energy consumption, and real-time adaptability. In one embodiment, the framework includes key components such as adaptive data preprocessing module 102, model training optimization module 104, and hyperparameter optimization module 106 to streamline data handling and training. The system also incorporates inference and deployment optimization module 108 and resource and energy efficiency module 110 for latency reduction and cost-effectiveness. Additional components such as self-optimizing feedback loop 114 and dynamic workload forecasting module 130 support seamless real-time processing and resource allocation across diverse AI/ML applications.

[00030] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with adaptive data preprocessing module 102, which dynamically adjusts data cleaning, transformation, and augmentation based on data quality and downstream task requirements. This module reduces preprocessing time and resources by selecting the most representative data subsets, essential for efficient training in model training optimization module 104. It also interworks with hyperparameter optimization module 106 to ensure that preprocessing aligns with model tuning parameters, creating an adaptive data pipeline that optimally prepares data for complex ML tasks.

[00031] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with model training optimization module 104, which employs parallelization and distributed training strategies to efficiently manage high-computation training workloads. The module leverages gradient accumulation and mixed-precision training to reduce memory usage and energy consumption, enhancing the pipeline's resource efficiency. It works closely with adaptive data preprocessing module 102 to ensure the processed data is effectively utilized during training, while interacting with resource and energy efficiency module 110 to monitor and optimize resource allocation.

[00032] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with hyperparameter optimization module 106, which accelerates model tuning through Bayesian optimization and genetic algorithms, significantly reducing the need for exhaustive search methods. This module also features an early stopping mechanism to terminate non-optimal configurations, thereby saving computational resources. It coordinates with model training optimization module 104 to fine-tune hyperparameters in real-time, optimizing model performance while managing computational load through resource and energy efficiency module 110.

[00033] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with inference and deployment optimization module 108, which improves real-time model inference by using model pruning and quantization, reducing latency and enabling deployment on edge devices. The module adapts its optimization strategies based on deployment requirements, such as batch or asynchronous processing, interacting with adaptive model retraining scheduler 124 to update the model as new data becomes available. It also works with energy and cost optimization module 122 to balance performance and resource use in diverse deployment environments.
[00034] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with resource and energy efficiency module 110, which manages the allocation of computational resources based on task priority and energy consumption profiles. This module utilizes an energy-aware scheduling system, ensuring resources are efficiently allocated without compromising performance. It collaborates with adaptive data preprocessing module 102 and model training optimization module 104 to streamline resource usage across pipeline stages, while integrating with green AI frameworks to monitor and adjust energy consumption dynamically.

[00035] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with scalability and real-time processing module 112, which enables the pipeline to handle large-scale data and real-time processing demands efficiently. This module uses incremental preprocessing and scalable distributed training methods to ensure that the system remains responsive under varying workloads. It coordinates with adaptive data preprocessing module 102 to manage data flow and dynamically adjusts resource use through resource and energy efficiency module 110, supporting seamless scalability across edge, cloud, and hybrid environments.

[00036] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with self-optimizing feedback loop 114, which continuously monitors pipeline performance metrics, such as accuracy, latency, and resource utilization, to automatically adjust parameters in real-time. This feedback loop interacts with model training optimization module 104 and hyperparameter optimization module 106 to refine learning rates, batch sizes, and other training settings dynamically, improving overall system efficiency. It also collaborates with resource and energy efficiency module 110 to optimize resource allocation based on current performance requirements.

[00037] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with context-aware data augmentation pipeline 116, which adapts data augmentation strategies based on real-time training conditions and resource availability. By dynamically adjusting augmentation techniques, such as noise addition or transformations, it ensures optimal data diversity without overloading the system. This pipeline works in conjunction with adaptive data preprocessing module 102 to enrich data inputs effectively and coordinates with self-optimizing feedback loop 114 to monitor augmentation impact on model performance.

[00038] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with cross-stage dependency optimization module 118, which maps resource dependencies across pipeline stages, such as data preprocessing, model training, and inference, to streamline inter-stage interactions. This module adjusts resource allocation and execution order based on real-time requirements, enhancing efficiency throughout the pipeline. It collaborates with adaptive data preprocessing module 102 and model training optimization module 104 to balance resources based on interdependent tasks, ensuring efficient end-to-end processing.

[00039] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with automated model deployment optimization engine 120, which dynamically selects and adjusts deployment strategies based on current network conditions, computational resources, and latency requirements. This engine enables efficient deployment across edge and cloud environments by customizing hardware configurations and batch sizes. It interacts with inference and deployment optimization module 108 to ensure deployment settings are continually optimized and collaborates with resource and energy efficiency module 110 to maintain an energy-efficient deployment.

[00040] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with energy and cost optimization module 122, which integrates energy consumption data with cost-effective resource allocation strategies to minimize operational costs. By analyzing real-time energy profiles, this module adjusts configurations across the pipeline to reduce energy expenses without sacrificing performance. It coordinates with resource and energy efficiency module 110 and automated model deployment optimization engine 120 to balance energy and cost, supporting sustainable and budget-friendly pipeline management.

[00041] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with adaptive model retraining scheduler 124, which triggers model retraining based on predefined criteria like data drift, performance degradation, or environmental changes. This scheduler optimizes retraining frequency to maintain model accuracy without overloading the system. It interacts with inference and deployment optimization module 108 for efficient model updates and with energy and cost optimization module 122 to defer non-critical retraining during high-demand periods, balancing performance and resource use.

[00042] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with multi-model parallel processing engine 126, which enables the simultaneous processing of multiple AI/ML models by prioritizing resource allocation based on model requirements. This engine allows for resource sharing across similar models, such as those performing related tasks, to optimize hardware usage. It works closely with model training optimization module 104 and cross-stage dependency optimization module 118 to distribute resources effectively and maintain high performance in parallel model tasks.

[00043] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with federated learning resource balancing module 128, which distributes computation between edge devices and cloud servers, optimizing for latency, network bandwidth, and energy availability in federated learning setups. This module dynamically adjusts data sharding and processing locations, maintaining efficient training across distributed environments. It collaborates with scalability and real-time processing module 112 and resource and energy efficiency module 110 to balance resource demands in real-time across federated networks.

[00044] Referring to Fig. 1, performance optimization framework for AI and ML pipelines 100 is provided with dynamic workload forecasting module 130, which predicts the computational demand for each pipeline stage based on real-time inputs and historical data trends. This module pre-allocates resources in advance to handle workload spikes, ensuring minimal delays and optimal throughput. It works alongside self-optimizing feedback loop 114 to continuously adjust resource allocation and interacts with scalability and real-time processing module 112 to ensure the pipeline can accommodate dynamic workload variations.

[00045] Referring to Fig 2, there is illustrated method 200 for performance optimisation framework for AI and machine learning pipeline 100. The method comprises:

At step 202, method 200 includes adaptive data preprocessing module 102 adjusting data cleaning, transformation, and augmentation based on input quality and task requirements;

At step 204, method 200 includes adaptive data preprocessing module 102 selecting representative data subsets for efficient processing, sending optimized data to model training optimization module 104;

At step 206, method 200 includes model training optimization module 104 utilizing parallelization, gradient accumulation, and mixed-precision to manage large data volumes while optimizing resources via resource and energy efficiency module 110;

At step 208, method 200 includes hyperparameter optimization module 106 using Bayesian optimization and genetic algorithms for optimal tuning, coordinating with model training optimization module 104 to enhance performance;

At step 210, method 200 includes self-optimizing feedback loop 114 adjusting parameters in real-time, fine-tuning learning rates and batch sizes within model training optimization module 104 for efficiency;

At step 212, method 200 includes context-aware data augmentation pipeline 116 dynamically managing augmentation based on training conditions, reinforcing data quality in adaptive data preprocessing module 102;

At step 214, method 200 includes inference and deployment optimization module 108 applying model pruning and quantization to reduce latency, preparing models for deployment across edge and cloud environments;

At step 216, method 200 includes automated model deployment optimization engine 120 selecting optimal configurations, adjusting for network and resource constraints, and deploying models cost-effectively with support from energy and cost optimization module 122;

At step 218, method 200 includes adaptive model retraining scheduler 124 monitoring performance and triggering retraining based on data drift, scheduling updates via resource and energy efficiency module 110;

At step 220, method 200 includes multi-model parallel processing engine 126 managing concurrent models on shared infrastructure, optimizing resource use through cross-stage dependency optimization module 118;

At step 222, method 200 includes federated learning resource balancing module 128 distributing computation between edge and cloud resources, adjusting data processing based on latency and bandwidth in coordination with scalability and real-time processing module 112;

At step 224, method 200 includes dynamic workload forecasting module 130 predicting computational demand, pre-allocating resources with self-optimizing feedback loop 114 to maintain optimal performance during workload changes.


[00046] 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.

[00047] 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.

[00048] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

, Claims:WE CLAIM:
1. A performance optimisation framework for AI and machine learning pipeline 100 comprising of
adaptive data preprocessing module 102 to adjust data cleaning, transformation, and augmentation based on data quality and task requirements;
model training optimization module 104 to manage training workloads using parallelization and gradient accumulation; hyperparameter optimization module 106 to fine-tune model parameters efficiently with advanced algorithms;
inference and deployment optimization module 108 to reduce latency and prepare models for diverse deployment environments;
resource and energy efficiency module 110 to optimize computational resource allocation and reduce energy consumption;
scalability and real-time processing module 112 to enable large-scale data handling and real-time processing;
self-optimizing feedback loop 114 to adjust pipeline parameters based on performance metrics in real-time; context-aware data augmentation pipeline 116 to manage augmentation strategies based on training conditions;
cross-stage dependency optimization module 118 to streamline resource use across dependent pipeline stages; automated model deployment optimization engine 120 to select deployment strategies based on network and resource conditions;
energy and cost optimization module 122 to reduce operational costs and energy usage across pipeline stages; adaptive model retraining scheduler 124 to monitor model performance and trigger retraining as needed;
multi-model parallel processing engine 126 to manage concurrent models on shared infrastructure;
federated learning resource balancing module 128 to distribute computation between edge and cloud resources effectively; and
dynamic workload forecasting module 130 to predict computational demands and pre-allocate resources efficiently.

2. The performance optimization framework for AI and ML pipelines 100 as claimed in claim 1, wherein adaptive data preprocessing module 102 is configured to dynamically adjust data cleaning, transformation, and augmentation based on real-time data quality and task-specific requirements, reducing preprocessing overhead and optimizing downstream data flow.

3. The performance optimization framework for AI and ML pipelines 100 as claimed in claim 1, wherein model training optimization module 104 is configured to utilize parallelization, gradient accumulation, and mixed-precision techniques to manage high-computation training workloads efficiently, enabling faster training with reduced memory and energy consumption.

4. The performance optimization framework for AI and ML pipelines 100 as claimed in claim 1, wherein hyperparameter optimization module 106 is configured to execute Bayesian optimization and genetic algorithms for rapid hyperparameter tuning, incorporating early stopping to eliminate non-optimal configurations and enhance computational efficiency.

5. The performance optimization framework for AI and ML pipelines 100 as claimed in claim 1, wherein inference and deployment optimization module 108 is configured to apply model pruning and quantization techniques to minimize latency, facilitating deployment on diverse edge and cloud environments without compromising model accuracy.

6. The performance optimization framework for AI and ML pipelines 100 as claimed in claim 1, wherein resource and energy efficiency module 110 is configured to manage real-time resource allocation based on energy consumption profiles, employing energy-aware scheduling to optimize power usage across the pipeline while maintaining performance standards.

7. The performance optimization framework for AI and ML pipelines100 as claimed in claim 1, wherein self-optimizing feedback loop 114 is configured to monitor pipeline performance metrics continuously, dynamically adjusting training parameters such as batch size and learning rate to maintain optimal resource efficiency.

8. The performance optimization framework for AI and ML pipelines 100 as claimed in claim 1, wherein automated model deployment optimization engine 120 is configured to select deployment configurations based on network conditions and computational constraints, optimizing batch sizes and processing modes to ensure cost-effective model deployment across environments.

9. The performance optimization framework for AI and ML pipelines 100 as claimed in claim 1, wherein dynamic workload forecasting module 130 is configured to predict computational demand based on real-time and historical data inputs, enabling pre-allocation of resources to accommodate workload fluctuations and ensure minimal latency.

10. The performance optimisation framework for AI and machine learning pipeline 100 as claimed in claim 1, wherein method comprises of
adaptive data preprocessing module 102 adjusting data cleaning, transformation, and augmentation based on input quality and task requirements;

adaptive data preprocessing module 102 selecting representative data subsets for efficient processing, sending optimized data to model training optimization module 104;

model training optimization module 104 utilizing parallelization, gradient accumulation, and mixed-precision to manage large data volumes while optimizing resources via resource and energy efficiency module 110;

hyperparameter optimization module 106 using Bayesian optimization and genetic algorithms for optimal tuning, coordinating with model training optimization module 104 to enhance performance;

self-optimizing feedback loop 114 adjusting parameters in real-time, fine-tuning learning rates and batch sizes within model training optimization module 104 for efficiency;

context-aware data augmentation pipeline 116 dynamically managing augmentation based on training conditions, reinforcing data quality in adaptive data preprocessing module 102;

inference and deployment optimization module 108 applying model pruning and quantization to reduce latency, preparing models for deployment across edge and cloud environments;

automated model deployment optimization engine 120 selecting optimal configurations, adjusting for network and resource constraints, and deploying models cost-effectively with support from energy and cost optimization module 122;

adaptive model retraining scheduler 124 monitoring performance and triggering retraining based on data drift, scheduling updates via resource and energy efficiency module 110;

multi-model parallel processing engine 126 managing concurrent models on shared infrastructure, optimizing resource use through cross-stage dependency optimization module 118;

federated learning resource balancing module 128 distributing computation between edge and cloud resources, adjusting data processing based on latency and bandwidth in coordination with scalability and real-time processing module 112;

dynamic workload forecasting module 130 predicting computational demand, pre-allocating resources with self-optimizing feedback loop 114 to maintain optimal performance during workload changes.

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