Hybrid prediction model by integrating machine learning techniques with MLOps

Abstract

Recent advancements in machine learning (ML) have sparked widespread interest in integrating DevOps capabilities into software and services within the information technology sector. This objective has compelled organizations to revise their development processes. We propose a ML operations model based on meta-ensembling algorithm for gradient boosting regressor with a case study of real estate price prediction. The train and test dataset is loaded with (1460,80) predictive variables, with the sale price as the target variable. The forecasting model is developed using an artificial neural network and a linear logistic regression model, such as LASSO, alongside with the Heroku tool for model deployment. The methodology addresses different steps of data pre-processing, and feature engineering, followed by feature selection, model building, evolution, creating, and calling application programming interfaces for deployment as IaaS, under research, development, and production environment phases. The model is built using the Anaconda Jupyter notebook with various Python libraries and Docker to ensure reproducibility and robustness. To ensure good business value, the performance of the proposed and implemented model is evaluated using different classification metrics, such as area under the curve-ROC for correct assessment measure, alongside accuracy metrics like mean squared error, root mean squared error, and R-squared. Our work serves as a useful reference for building and deploying ML pipeline platforms in practice.