Abstract:
"The proliferation of digital identities and the increasing complexity of IT environments have underscored the necessity for advanced deployment strategies for identity servers. ""IDeploySmart"" presents an innovative machine learning-based approach to predict optimal deployment types, enhancing performance, scalability, and security. This research utilizes robust machine learning techniques, including RandomForestClassifier and Gradient Boosting, to develop a predictive model based on historical deployment data and current performance metrics.
Initial evaluations of ""IDeploySmart"" demonstrate a model accuracy of 92.5%, indicating its potential to make reliable deployment predictions. Comprehensive preprocessing and feature engineering were integral to this project's success, addressing data imbalance and feature scaling. Hyperparameter tuning using GridSearchCV further optimized the model's performance by systematically exploring the best parameter combinations.
Benchmarking against industry-standard models such as RandomForest, XGBoost, and LightGBM revealed that ""IDeploySmart"" outperforms these models in key performance metrics. Expert interviews and real-world validation confirmed the model's practical applicability, highlighting its potential impact on improving identity server deployment strategies. This thesis underscores the transformative potential of machine learning in IT infrastructure management, providing a scalable, efficient, and secure solution for deploying identity servers. ""IDeploySmart"" sets a new standard for predictive modeling in this domain, paving the way for future advancements in integrating machine learning with IT operations."
