Deployment Of Machine Learning Models Research Articles

A WebGIS-Based System for Supporting Saline-Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China

The monitoring and evaluation of soil ecological environment is very important to ensure the saline-alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent needs of high-performance prediction of soil properties using multi-dimensional data in WebGIS system. For the end, this study aims to develop a saline-alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction and data management. The system visually displays the statistical results of air quality, vegetation index and soil properties in the study area. It provides users with ecological risk assessment functions to analyze soil heavy metal pollution. Specially, the system introduces a tree structured Parzan estimator (TPE) optimized machine learning model to achieve accurate prediction of soil salinity. The TPE-RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE-GBDT model, which exhibited the strong ability for the nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline-alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline-alkali soil ecological protection.

Business text classification with imbalanced data and moderately large label spaces for digital transformation

Digital transformation refers to an organization’s use of digital technology to improve its products, services, and operations, aligning them with evolving business requirements. To demonstrate this transformative process, we present a real-life case study where a company seeks to automate the classification of their textual data rather than relying on manual methods. Transitioning to automated classification involves deploying machine learning models, which rely on pre-labeled datasets for training and making predictions on new data. However, upon receiving the dataset from the company, we faced challenges due to the imbalanced distribution of labels and moderately large label spaces. To tackle text classification with such a business dataset, we evaluated four distinct methods for multi-label text classification: fine-tuned Bidirectional Encoder Representations from Transformers (BERT), Binary Relevance, Classifier Chains, and Label Powerset. The results revealed that fine-tuned BERT significantly outperformed the other methods across key metrics like Accuracy, F1-score, Precision, and Recall. Binary Relevance also displayed competence in handling the dataset effectively, while Classifier Chains and Label Powerset exhibited comparatively less impressive performance. These findings highlight the remarkable effectiveness of fine-tuned BERT model and the Binary Relevance classifier in multi-label text classification tasks, particularly when dealing with imbalanced training datasets and moderately large label spaces. This positions them as valuable assets for businesses aiming to automate data classification in the digital transformation era.

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Researchers worldwide have been focusing on accurately predicting the remaining useful life of electronic devices to ensure reliability in various industries. This has been made possible by advancements in artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies. However, accurately forecasting device life with minimal data sets, especially in industrial applications, remains a challenge. This paper aims to address this challenge by utilizing machine learning algorithms, specifically BP, XGBOOST, and KNN, to predict device reliability with limited data. The remaining life dataset of electronic components is obtained through simulation for training and testing the algorithms, and the experimental results show that the algorithms achieve a certain level of accuracy, with the error rates being as follows: BP algorithm, 0.01–0.02%; XGBOOST algorithm, 0.01–0.02%; and KNN algorithm, 0–0.07%. By benchmarking these algorithms, the study demonstrates the feasibility of deploying machine learning models for device life prediction with acceptable accuracy loss, and highlights the potential of AI algorithms in predicting the reliability of electronic devices.

УСПЕХИ В МОНИТОРИНГЕ СОСТОЯНИЯ КОНСТРУКЦИЙ: ОБЗОР ПОДХОДОВ МАШИННОГО ОБУЧЕНИЯ ДЛЯ ОБНАРУЖЕНИЯ И ОЦЕНКИ ПОВРЕЖДЕНИЙ

Structural Health Monitoring (SHM) is a crucial discipline geared towards detecting damage in engineering structures early, aiming to prevent failures and facilitate condition-based maintenance. Traditional SHM methodologies, relying on visual inspections, analytical models, and signal processing, exhibit inherent limitations. The advent of machine learning has introduced data-driven solutions to automate various aspects of SHM, including damage detection, localization, classification, and prognosis.
 This paper provides a comprehensive review of recent studies exploring supervised, unsupervised, and deep learning techniques in vibration-based, image-based, and multi-sensor SHM. Support vector machines, neural networks, deep convolutional neural networks, and other advanced algorithms have demonstrated exceptional performance in assessing damage using real-world structural datasets.
 Despite these successes, practical challenges persist, particularly in addressing variability and deploying machine learning models effectively on full-scale structures. Overcoming these challenges necessitates a more integrated, cross-disciplinary approach, merging mechanical engineering fundamentals with machine learning expertise. This synergy can pave the way for robust field implementation and further enhance the reliability of SHM systems.
 The transformative potential of machine learning in SHM cannot be understated. Beyond merely shifting from time-based maintenance to condition-based strategies, machine learning can automate and continuously evaluate structural integrity, ensuring the longevity of engineering structures. As we delve deeper into the intersection of mechanical engineering and machine learning, the prospect of a future where SHM seamlessly integrates with advanced technologies becomes increasingly tangible.

Learning by Erasing: Conditional Entropy Based Transferable Out-of-Distribution Detection

Detecting OOD inputs is crucial to deploy machine learning models to the real world safely. However, existing OOD detection methods require an in-distribution (ID) dataset to retrain the models. In this paper, we propose a Deep Generative Models (DGMs) based transferable OOD detection that does not require retraining on the new ID dataset. We first establish and substantiate two hypotheses on DGMs: DGMs exhibit a predisposition towards acquiring low-level features, in preference to semantic information; the lower bound of DGM's log-likelihoods is tied to the conditional entropy between the model input and target output. Drawing on the aforementioned hypotheses, we present an innovative image-erasing strategy, which is designed to create distinct conditional entropy distributions for each individual ID dataset. By training a DGM on a complex dataset with the proposed image-erasing strategy, the DGM could capture the discrepancy of conditional entropy distribution for varying ID datasets, without re-training. We validate the proposed method on the five datasets and show that, without retraining, our method achieves comparable performance to the state-of-the-art group-based OOD detection methods. The project codes will be open-sourced on our project website.

Analysis of Diabetes Prediction using Decision Tree Classifier

Diabetes is a serious complaint that affects the maturity of the population. Now a days its play a major role on human life. Imbalance in insulin processing by the body which leads to varieties of disorder. The main aim of this work is to make an early prediction of diabetes more precisely by using Auto Machine Learning Tools. Auto Machine learning Tools provide better results in diabetes detection by constructing models from patient datasets. This model automates the training, tuning, and deploying machine learning models. Recent developments in Machine learning show that Automatic Diabetic detection using Random Forest Algorithm models can be very beneficial in such problems. The proposed Random forest model predicts the diabetes at early stage. We use Decision tree classifier to predict whether a patient has diabetes based on diagnostic measurements. Performance and accuracy of the applied algorithm is discusses and compared.

Maintaining Symmetry between Convolutional Neural Network Accuracy and Performance on an Edge TPU with a Focus on Transfer Learning Adjustments

Transfer learning has proven to be a valuable technique for deploying machine learning models on edge devices and embedded systems. By leveraging pre-trained models and fine-tuning them on specific tasks, practitioners can effectively adapt existing models to the constraints and requirements of their application. In the process of adapting an existing model, a practitioner may make adjustments to the model architecture, including the input layers, output layers, and intermediate layers. Practitioners must be able to understand whether the modifications to the model will be symmetrical or asymmetrical with respect to the performance. In this study, we examine the effects of these adjustments on the runtime and energy performance of an edge processor performing inferences. Based on our observations, we make recommendations for how to adjust convolutional neural networks during transfer learning to maintain symmetry between the accuracy of the model and its runtime performance. We observe that the edge TPU is generally more efficient than a CPU at performing inferences on convolutional neural networks, and continues to outperform a CPU as the depth and width of the convolutional network increases. We explore multiple strategies for adjusting the input and output layers of an existing model and demonstrate important performance cliffs for practitioners to consider when modifying a convolutional neural network model.

BenchMFC: A benchmark dataset for trustworthy malware family classification under concept drift

Concept drift poses a critical challenge in deploying machine learning models to mitigate practical malware threats. It refers to the phenomenon that the distribution of test data changes over time, gradually deviating from the original training data and degrading model performance. A promising direction for addressing concept drift is to detect drift samples and then retrain the model. However, this field currently lacks a unified, well-curated, and comprehensive benchmark, which often leads to unfair comparisons and inconclusive outcomes. To improve the evaluation and advance further, this paper presents a new Benchmark dataset for trustworthy Malware Family Classification (BenchMFC), which includes 223 K samples of 526 families that evolve over years. BenchMFC provides clear family, packer, and timestamp tags for each sample, it thus can support research on three types of malware concept drift: 1) unseen families, 2) packed families, and 3) evolved families. To collect unpacked family samples from large-scale candidates, we introduce a novel crowdsourcing malware annotation pipeline, which unifies packing detection and family annotation as a consensus inference problem to prevent costly packing detection. Moreover, we provide two case studies to illustrate the application of BenchMFC in 1) concept drift detection and 2) model retraining. The first case demonstrates the impact of three types of malware concept drift and compares nine notable concept drift detectors. The results show that existing detectors have their own advantages in dealing with different types of malware concept drift, and there is still room for improvement in malware concept drift detection. The second case explores how static feature-based machine learning operates on packed samples when retraining a model. The experiments illustrate that packers do preserve some kind of signals that appear to be “effective” for machine learning models, but the robustness of these signals requires further research. BenchMFC has been released to the community at https://github.com/crowdma/benchmfc.

Neural Networks based Smart e-Health Application for the Prediction of Tuberculosis using Serverless Computing.

The convergence of the Internet of Things (IoT) with e-health records is creating a new era of advancements in the diagnosis and treatment of disease, which is reshaping the modern landscape of healthcare. In this paper, we propose a neural networks-based smart e-health application for the prediction of Tuberculosis (TB) using serverless computing. The performance of various Convolution Neural Network (CNN) architectures using transfer learning is evaluated to prove that this technique holds promise for enhancing the capabilities of IoT and e-health systems in the future for predicting the manifestation of TB in the lungs. The work involves training, validating, and comparing Densenet-201, VGG-19, and Mobilenet-V3-Small architectures based on performance metrics such as test binary accuracy, test loss, intersection over union, precision, recall, and F1 score. The findings hint at the potential of integrating these advanced Machine Learning (ML) models within IoT and e-health frameworks, thereby paving the way for more comprehensive and data-driven approaches to enable smart healthcare. The best-performing model, VGG-19, is selected for different deployment strategies using server and serless-based environments. We used JMeter to measure the performance of the deployed model, including the average response rate, throughput, and error rate. This study provides valuable insights into the selection and deployment of ML models in healthcare, highlighting the advantages and challenges of different deployment options. Furthermore, it also allows future studies to integrate such models into IoT and e-health systems, which could enhance healthcare outcomes through more informed and timely treatments.

Machine Learning as a Service Cloud Selection: An MCDM Approach for Optimal Decision Making

Machine Learning as a Service, MLaaS refers to cloud-based platforms that provide machine learning tools and infrastructure to users, allowing them to access and utilize machine learning capabilities without managing the underlying hardware and software. Cloud service providers offer scalable and flexible resources, enabling businesses to train and deploy machine learning models efficiently and cost-effectively. Multi-Criteria Decision making, MCDM helps individuals or organizations make choices when multiple criteria or objectives are involved. It aims to find the best alternative among a set of available options by considering various criteria or factors that are relevant to the decision. Machine learning as a service (MLaaS) and MCDM are two distinct concepts that can be combined to create powerful decision-making solutions. The present paper proposed an integrated approach based on the Analytic Hierarchy Process and TOPSIS method for choosing the best MLaaS cloud. Four alternatives for MLaaS cloud were analyzed based on seventeen shortlisted criteria. Based on the calculations, conclusions have been reached and potential study areas have been identified.

A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning

Worldwide, heart attacks, also called myocardial infarctions, are a leading cause of death. Early detection and accurate prediction of heart attacks are crucial for effective medical intervention and patient care. In recent years, machine learning techniques have shown great promise in aiding the diagnosis and prediction of heart attacks. The Organization for World Health (WHO) reports that around 17 million individuals worldwide pass away from cardiovascular diseases (CVD), notably heart attacks and strokes, each year. In this study, 1026 patients, both men and women, are almost equally affected by CVDs. While heart attacks and strokes remain among the leading causes of mortality worldwide, the use of machine learning for predicting heart disease could potentially prevent premature deaths. A comparative study evaluated the performance of five well-known two-class classification algorithms: two-class boosted decision trees, two-class decision forests, two-class locally deep SVMs, two-class neural networks, and two-class logistic regression. Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. Its exceptional recall and precision rates highlight its effectiveness in handling challenging classifications. To facilitate the development and deployment of machine learning models, Azure Machine Learning offers a range of tools and services. By leveraging Azure Machine Learning's capabilities, researchers and healthcare professionals can analyze large datasets containing patient information and medical records to identify patterns and risk factors associated with heart attacks.

Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold?

Medical applications of machine learning (ML) have shown promise in analyzing patient data to support clinical decision-making and provide patient-specific outcomes. In transplantation, several applications of ML exist which include pretransplant: patient prioritization, donor-recipient matching, organ allocation, and posttransplant outcomes. Numerous studies have shown the development and utility of ML models, which have the potential to augment transplant medicine. Despite increasing efforts to develop robust ML models for clinical use, very few of these tools are deployed in the healthcare setting. Here, we summarize the current applications of ML in transplant and discuss a potential clinical deployment framework using examples in organ transplantation. We identified that creating an interdisciplinary team, curating a reliable dataset, addressing the barriers to implementation, and understanding current clinical evaluation models could help in deploying ML models into the transplant clinic setting.

A review of on-device machine learning for IoT: An energy perspective

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications.

4TH INDUSTRIAL REVOLUTION AND ARTIFICIAL INTELLIGENCE

An overview of industrial revolutions is made. Then the analysis of the fourth industrial revolution. The main major components of Industry 4.0 are identified, and their essence is revealed. The tasks of artificial intelligence within Industry 4.0 are also considered. Developed software in Python language about data and machine learning (ML) to visualize objects and table data. In order to use the capabilities of AI to the maximum benefit for business, it is necessary to hire data scientists. Data science combines statistics, computer science and business knowledge to extract value from various data sources. Developers use artificial intelligence to more effectively perform tasks that would otherwise have to be done manually, interact with customers, identify patterns and solve problems. To start working with AI, developers will need mathematical knowledge and the ability to use algorithms. There are several stages in the development and deployment of machine learning models, including training and inference. AI learning and inference refers to the process of experimenting with machine learning models to solve a problem. For example, a machine learning engineer can experiment with various candidate models to solve a computer vision problem, such as detecting bone fractures in X-ray images. Keywords: fourth industrial revolution, artificial intelligence.

Machine Learning Operations (MLOps): Challenges and Strategies

Machine Learning Operations (MLOps) is a critical discipline that aims to streamline and enhance the end-to-end machine learning (ML) lifecycle, encompassing development, deployment, monitoring, and maintenance. As organizations increasingly adopt machine learning models to derive actionable insights and automate decision-making, MLOps becomes indispensable for ensuring efficiency, scalability, and reliability in ML workflows. This abstract explores the challenges encountered in implementing MLOps and presents strategies to overcome these hurdles.The challenges in MLOps can be categorized into technical, organizational, and cultural aspects. Technical challenges include model versioning, reproducibility, and ensuring consistent performance across diverse environments. Organizational challenges involve collaboration between cross-functional teams, managing diverse tools and frameworks, and integrating ML workflows with existing software development processes. Cultural challenges encompass resistance to change, skill gaps, and the need for a shared understanding of ML concepts among stakeholders.To address these challenges, a multifaceted strategy is proposed. Implementing robust version control systems and containerization techniques can enhance model reproducibility and deployment consistency. Cross-functional collaboration can be fostered through the establishment of dedicated MLOps teams, emphasizing communication and knowledge sharing. The integration of MLOps into existing DevOps practices can streamline workflows and mitigate organizational silos.Furthermore, the adoption of automated testing, continuous integration, and continuous deployment practices specific to ML can bolster the reliability of ML systems. Education and upskilling programs can bridge skill gaps, while promoting a culture of continuous learning and adaptability. Open-source MLOps tools and frameworks contribute to standardization and interoperability, facilitating smoother integration into diverse ecosystems.
 
 In conclusion, the successful implementation of MLOps is crucial for organizations seeking to harness the full potential of machine learning. By addressing technical, organizational, and cultural challenges through a comprehensive strategy, businesses can establish resilient and efficient MLOps pipelines, paving the way for sustainable and scalable deployment of machine learning models.

Image recognition in self-driving cars using CNN

The concept of neural networks has existed for over decades but was never considerably acknowledged as much as of today. The main reason happens to be “data.” To analyze a problem statement using neural networks, large data is required in its various forms and therefore it has not been instigated back in the day. But now, with today’s vast technology, neural networks have begun to take over some of the numerous machine learning applications with the help of huge datasets. In this research paper, a certain deep learning approach namely convolutional neural network (CNN) has been discussed which plays a major role in classifying and recognizing objects i.e., obstacles on the road. Earlier, computer-based algorithms have been followed for image processing in vehicles which seemed to be applicable to a certain extent. So much so, now with deep learning approaches, simpler yet faster networks can be implemented for a safe drive. Automatic vehicles such as Tesla which is examined to be “fully self-driving” nevertheless needs a driver to watch over the road at some particular point. This proves that there is not yet a fully controlled self-driving car created which can drive itself without a spectator. This appeal can be solved by means of image detection mechanisms using neural networks along with a programming language to deploy machine learning models at ease. The main objective is to develop a simple and accurate algorithm to make image recognition more precise for a better self-driving car.

Continuous Risk Estimation of Acute Kidney Failure with Dense Temporal Data for ICU Patients.

Acute kidney failure is a dangerous complication for ICU patients, and it is difficult to identify at early stage with conventional medical analysis. In recent years, machine learning approaches have been applied to tackle medical diagnosis tasks with great performance. In this work, we deploy machine learning models for early detection of acute kidney failure that can handle static, temporal, sparse and dense data of ICU patients. We investigate different pre-processing methods for patient data to achieve higher prediction performance and how they influence the contribution of different physiological signals in the prediction process.

NNPred: Deploying neural networks in computational fluid dynamics codes to facilitate data-driven modeling studies

Data-driven modeling has contributed significantly to the field of computational fluid dynamics (CFD), but integrating machine-learning (ML) models into a CFD workflow still remains to be a challenging task. In this paper, we introduce an interface library for the deployment of ML models in CFD codes. The library supports multiple ML backends with binary interface compatibility and provides flexible data input/output (I/O) methods to match the type and layout of the data between ML models and CFD codes. For the convenience of CFD users, the library provides application programming interfaces for two widely used languages (i.e., C++ and Fortran), simplifying the deployment and prediction to only a few lines of code. Two data-driven modeling cases are demonstrated, along with the implementation details of the library in open-source CFD codes (i.e., OpenFOAM and CFL3D). The first case presents a simple heat-transfer problem with assumed experimental data of unknown emissivity, where the basic use of the library in OpenFOAM is demonstrated by solving a diffusive equation with the source term modeled by various ML algorithms. The second case discusses the modeling of turbulence in channel flow, and applies an ML-integrated closure model in both OpenFOAM and CFL3D. In both software, the ML-RANS model reproduces almost identical results to the reference data and favorable extrapolation performance is maintained. In addition, the parallel efficiency is compared with the traditional closure model under the same homogeneous parallel architecture, and only limited additional cost is imposed by running the ML prediction. This library could benefit CFD researchers in rapidly testing data-driven models in their CFD solvers. Program summaryProgram Title: NNPredCPC Library link to program files:https://doi.org/10.17632/v2jzcyjn8v.1Developer's repository link:https://github.com/Weishuo93/NN_PredLicensing provisions: MIT1, LGPL2, Apache-2.03Programming language: C++, FortranNature of problem: Coupling machine learning models with CFD programs with minimal modifications to facilitate the study of data-driven modeling methods (e.g., rapid modeling from experimental data, turbulence closure modeling, etc) with MPI supports.Solution method: First, an interfacing library is developed to bridge ML modeling and a CFD solver. The library supports different ML backends and provides flexible data I/O methods when interfacing with CFD codes. The API of this library can be accessed through C++ and Fortran, which are two most commonly used languages in the CFD community. Secondly, two data-driven modeling cases are demonstrated with the library's implementation details in OpenFOAM and CFL3D. The first case shows a semi-empirical modeling problem in heat transfer with assumed experimental data, whereas the second case reproduces the results of DNS with an ML-integrated turbulence model in two different CFD software (OpenFOAM and CFL3D). Finally, parallel computation is conducted using a homogeneous Message Passing Interface, which is adopted by most mainstream CFD programs. The extra costs are observed limited, and therefore users could validate their ideas on data-driven models with great convenience on parallel programs.Additional comments including restrictions and unusual features: Heterogeneous parallel strategy with GPU and associated applications need to be further developed.

Aligning Federated Learning with Existing Trust Structures in Health Care Systems.

Patient-centered health care information systems (PHSs) on peer-to-peer (P2P) networks (e.g., decentralized personal health records) enable storing data locally at the edge to enhance data sovereignty and resilience to single points of failure. Nonetheless, these systems raise concerns on trust and adoption in medical workflow due to non-alignment to current health care processes and stakeholders' needs. The distributed nature of the data makes it more challenging to train and deploy machine learning models (using traditional methods) at the edge, for instance, for disease prediction. Federated learning (FL) has been proposed as a possible solution to these limitations. However, the P2P PHS architecture challenges current FL solutions because they use centralized engines (or random entities that could pose privacy concerns) for model update aggregation. Consequently, we propose a novel conceptual FL framework, CareNetFL, that is suitable for P2P PHS multi-tier and hybrid architecture and leverages existing trust structures in health care systems to ensure scalability, trust, and security. Entrusted parties (practitioners' nodes) are used in CareNetFL to aggregate local model updates in the network hierarchy for their patients instead of random entities that could actively become malicious. Involving practitioners in their patients' FL model training increases trust and eases access to medical data. The proposed concepts mitigate communication latency and improve FL performance through patient-practitioner clustering, reducing skewed and imbalanced data distributions and system heterogeneity challenges of FL at the edge. The framework also ensures end-to-end security and accountability through leveraging identity-based systems and privacy-preserving techniques that only guarantee security during training.

Study of Delay Prediction in the US Airport Network

In modern business, Artificial Intelligence (AI) and Machine Learning (ML) have affected strategy and decision-making positively in the form of predictive modeling. This study aims to use ML and AI to predict arrival flight delays in the United States airport network. Flight delays carry severe social, environmental, and economic impacts. Deploying ML models during the process of operational decision-making can help to reduce the impact of these delays. A literature review and critical appraisal were carried out on previous studies and research relating to flight delay prediction. In the literature review, the datasets used, selected features, selected algorithms, and evaluation tools used in previous studies were analyzed and influenced the decisions made in the methodology for this study. Data for this study comes from two public sets of domestic flight and weather data from 2017. Data are processed and split into training, validation, and testing data. Subsequently, these ML models are evaluated and compared based on performance metrics obtained using the testing data. The predictive model with the best performance (in choosing between logistic regression, random forest, the gradient boosting machine, and feed-forward neural networks) is the gradient boosting machine.