Description:Field of Investigation
The invention deals with the fields of artificial intelligence and machine learning, concentrating on automated machine learning (AutoML) systems. It intends to address problems associated with the construction, optimization, and deployment of machine learning models, which it proposes to do by automating critical stages of the pipeline such as data preprocessing, feature engineering, algorithm selection, hyperparameter tuning, and model evaluation. Explainable AI (XAI) capabilities are integrated to address the challenge of maintaining transparency and interpretability concerning machine learning models. These large domains of the inquiry combine software engineering, data science, and AI-driven analytics targeting applications in a wide array of domains, from health-care and finance to retail and industrial automation that are fast requiring efficient yet accessible AI solutions.
Background of the Invention
Their electrospinning capabilities might transform industries comparatively dramatically using prediction and automation solutions that machine learning provides. Yet, structuring machine learning models involves quite-a-few different tasks, from data preprocessing and algorithm selection to tuning hyperparameters and deploying the model, that are all equally demanding. All of these demand time and technical know-how, which would hinder their access to an organization or an individual without active resources. Further, why it is not done is that the transition of explanations to decision-making in terms of machine learning models becomes very opaque, a hurdle for acceptance and trust in very sensitive applications in healthcare or any finance area.
Existing solutions address some of these user challenges by providing workflows of partially automated, cloud-source, or stand-alone software and processes directed toward agricultural scientists without requisite experience in machine learning. Thus, traditional AutoML processes normally lack the overall flexibility required to adapt to different datasets, varying user requirements, and problem domains. In addition, very few systems that have the capacity of integrating explainability components leave a serious gap toward comprehensively understanding the decision-making process. Accordingly, the present invention addresses the limitations mentioned by providing a complete AutoML workflow with integrated Explainable AI modules that's straightforward to use. In this way, the long workflow of machine learning is implemented to provide easy accessibility of advanced machine learning within the realm of transparency and reliability.
US12061963B1 introduces techniques for automating the exploration and deployment of machine learning (ML) pipelines. These automated systems simplify the process of building optimized ML pipelines, allowing users to construct them effortlessly by simply providing a dataset, specifying a target column, and setting an exploration budget.The system explores multiple candidate ML pipelines, evaluates their performance, and ultimately selects the best one for the user's needs or production deployment. Users can remain actively involved throughout the process, with options to configure, monitor, and adapt the exploration as needed, ensuring the solution aligns with their goals. This approach makes sophisticated ML pipeline development more accessible and efficient.
US20200380416A1 processes of modeling methods are compounded in racks for machine learning pipeline created to make optimization for performance more straightforward using modelling methods for putting machine learning design into an object-oriented modelling (OOM) framework, the process includes: program of classes with object-oriented modelling of optimization methods, modelling methods and modelling racks; write parameter and hyper-parameters of the modelling methods like attributes of modeling methods; scan modelling racks classes to establish first class definition information; collection of rack selection and modelling method object; scan modeling method classes to create second-class definition information; assigning racks and also locations within the racks to modeling method objects; and invoke the class definition information to produce object manipulation functions that allow access to the techniques and characteristics of at least a few of the modeling method objects, with the manipulation functions set up to write locations and attributes of racks.

EP4128089B1 describes an ML pipeline as a sequence of steps designed to process input data and produce an ML inference result. This process may involve tasks like feature grouping, target processing, or building a "feature preprocessing pipeline" as part of the larger ML workflow. The pipeline might also include the use of one or more ML models before reaching the final inference model.Using an ML orchestrator, the system (referred to as the CML service) creates a fully trained, functional ML pipeline that supports both batch and real-time inference. For more advanced or curious users, the service offers a deeper dive into the details of the created ML model, including information about the training jobs, the parameters and steps within the pipeline, and insights into other explored pipelines that performed less effectively.Additionally, the CML service can generate feature preprocessing code and interactive exploration notebooks to help users understand and learn about the pipeline’s components. This "white box" approach transparently shows the steps and processes the system executed to develop the final ML model. It also gives users the flexibility to fine-tune the pipeline if needed, empowering them to customize it further based on their requirements.
US11625648B2 presents systems and methods for an adaptive pipelining composition service that can identify and incorporate one or more new models into the machine learning application. Off-line testing may be done on the machine learning application which had the new model, and the performance of that model is compared with true or established dataset. If the newly integrated model performs better than the old model, the application is upgraded into production mode. Additionally, one or more parameters might be found. New parameters incorporated into the existing model may be evaluated off-line, and the results may be compared with results using existing parameters. If the machine learning program performs better with the new parameters than with the old ones utilizing the ground truth data, it may be automatically promoted to production.
US11651216B2 gives instance of such embodiments will classify features from the training dataset and map costs thereof to those features. The search space could consist a generative identification of starting or seed candidates chosen based on one or more objectives and/or limitations. Upon selection of candidates, an iterative search for their optimization may take place until a stopping criterion is satisfied. Optimizations can be conducted by submission to an external optimizer. In particular, the external optimizer would apply constraints to the candidates in an iterative manner in order to compute a fitness level for each of the seed candidates. It may take those constraints along with those objective functions into consideration. The candidates may be a data set, or trained by a learning system forming explainable models or interpretable forms. The external optimizer optimizes these explainable models to meet, in the first instance, these stopping conditions.
Summary of the Invention
A comprehensive AutoML pipeline that encapsulates the entire machine learning workflow-from data ingestion to model deployment-is introduced in this invention. Key activity such as data preprocessing, feature engineering, model selection, hyperparameter tuning, and evaluation can be automated, greatly alleviating manual effort and expertise. The design of the system is aimed at facilitating user interaction through a web-based application. Users can upload datasets, specify target variables, and receive fully trained machine learning models for deployment. The platform is crafted to handle tabular data as a whole while addressing a wide spectrum of regression and classification problems.
One of the new aspects of this invention is its incorporation of XAI modules like LIME and SHAP in improving the explainability of the models being generated. This is believed to bring about trust and transparency by revealing to users the rationale behind model predictions. Great support has been embedded for exporting learned models to reusable formats, allowing the end-user to do batch prediction and deploying them into a real-life setting. This invention democratizes the machine learning powers, by permeating automation, explainability, and human-centric design, to allow a cross-industrial appeal, where even workers with a minor technical background can utilize it.
The invention also brings forth flexibility and scalability, allowing the handling of a vast array of datasets and problem domains. State-of-the-art machine learning techniques are integrated to choose ultimate algorithms and build specific pipelines for the task, ensuring maximum performance with minimum computation overhead. The platform could easily integrate into any existing data infrastructure, rendering it yet another tool for organizations that urge better productivity and more innovation through the AI-driven paradigm.
Brief Description of the Drawings
The invention will be described in detail with reference to the exemplary embodiments shown in the figures wherein:
Figure-1: Flow chart representing the work flow of the system
Figure-2: Architecture of the LIME based prediction model
Detailed Description of the Invention
At its core, the system has an automated pipeline that strings together the processes of collecting and preprocessing the data. The pipeline will accept a multitude of datasets in various formats, including CSV and JSON, and will execute various preprocessing steps, including handling missing values, encoding categorical variables, and scaling numerical variables. Robust techniques, including median imputation for missing data and one-hot encoding for categorical variables, ensure that the data is tidy and clean. The preprocessing unit also incorporates techniques for outlier detection and remediation to optimize data for downstream applications.
The system dynamically determines the target variable and its type, while the prediction model would have to be identified through user inputs. In other words, if the target were specified to be a column, the characteristics of that column would be analyzed. If the column has numerical values, it implies a regression task; otherwise, it implies classification tasks. This approach eliminates a lot of manual effort required to identify columns and understand possible model fitting types and thus aids in integrating numerous machine-learning libraries with minimal effort.
The next parameter is feature engineering, extracted and transformed to enhance model performance. The pipeline applies techniques like polynomial feature generation, interaction terms, and dimensionality reduction by Principal Component Analysis (PCA) to pull out hidden relationships from the dataset. These engineered features lead to power improvements in the performance and generalization of the model.
When constructing the AutoML-Pipeline, various algorithms are evaluated: linear models, decision trees, ensemble methods, and neural networks, while the final cross-validation process followed by hyperparameter optimization ensures that the most optimal model is selected for the task. These may include, but are not limited to, stacking and bagging in Python. The very nature of these variations leads to improved prediction reliability as well as resolution of more complex tasks.
An essential aspect of the invention is its integration with explainable AI (XAI) techniques. Tools like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP are embedded to provide insights into model predictions. Fostering trust and transparency in machine learning applications through visualization of feature importance and understanding model behavior unto the user are the purposes of the modules of XAI.
A prediction module allows users to deploy the trained models for real-time predictions. Thus, the system allows batch and streaming predictions, adapting to different use cases. Model artifacts-such as weights and preprocessing pipelines-are saved in a modular format, thereby enabling easy integration with other applications.
Additionally, the integrated system allows for the analysis of multimodal data, such as structured, unstructured, and semi-structured data. This makes the system applicable to various fields, ranging from healthcare, finance, and manufacturing. It revolutionizes machine learning by the full automation of the processes of building and deploying models, reducing time, cost, and expertise in the entire machine learning pipeline. Indeed, AutoML-Pipeline is a groundbreaking tool for data-driven decision-making.
The AutoML-Pipeline is oriented to revolutionize the whole machine-learning procedure through automation, interpretability, and scalability that work together in collaboration. Intuitive enough, enhanced through algorithms and XAI modules, it gives everyone, organizations, or simple individuals the tools that harness machine learning without a lot of friction.
Equivalents
The AutoML-Pipeline is conceptually similar to such existing AutoML inventions as Google AutoML, H2O.ai, and Auto-sklearn. The platforms mostly include automation for various stages of the ML pipeline, including data preprocessing, mixture selection, model training, and hyperparameter optimization. Other sections that ordinary augment AutoML Pipeline may be rankings of catalog displays alongside robust explainability tools such as LIME and SHAP, and support for multimodal datasets, whereas the current invention far surpasses its counterparts. In contrast to a lot of these relatives which demand costly computers or expertise, the entire system tends to emphasize scalability as well as serviceability together with seamless integration with the aim to enable the numerous users across different industries with varying technical capacities to leverage the major important functionalities at any point in time. , Claims:The scope of the invention is defined by the following claims:
Claim:
1. The Automl-pipeline: end-to-end automated machine learning workflow optimization comprising:
a) A fully automated machine learning pipeline that simplifies the entire process from dataset upload to model training and deployment.
b) The user-friendly front-end UI allows users to upload datasets effortlessly, while the backend performs automated model training with minimal manual intervention. This feature significantly reduces the complexity and time required for building robust machine learning models, making advanced ML techniques accessible to users with varying levels of expertise.
c) The integration of LIME (Local Interpretable Model-Agnostic Explanations) and SHAP(Shapley Additive Explanations)modules in the workflow provides users with clear and intuitive visualizations of model predictions.

2. According to claim 1, the LIME enhances the interpretability of the machine learning models, allowing users to understand and trust the model's decisions. By providing explanations for individual predictions, the invention addresses the critical need for transparency in AI, fostering user confidence and facilitating informed decision-making.

3. As per claim 1, the invention streamlines the model deployment process by enabling users to download the trained models in a compressed zip format directly from the UI. After training the model, users can easily access the download button, simplifying the transition from model development to deployment. This feature ensures that users can quickly and efficiently deploy their models in various applications, enhancing the practical utility and scalability of the solution.