Auto-ML Tools and Process on Google, Azure and IBM Cloud Platforms

The future of Automated Machine Learning (Auto ML) has improved the creativity for data scientists, ML engineers and ML researchers by reducing repetitive tasks in machine learning pipelines. Auto ML was designed to effectively solve problems of classification, Multiclass classification, complex systems behavior prediction and selecting unknown parameters that relate the characteristics of complex objects The objective of this research study was to predict the different types of network attacks in a network using the different cloud platforms; Google, Azure and IBM cloud platforms. We applied the multiclass classification machine learning to train and make predictions of the types of attacks in the UNSWNB15 network security dataset. We utilized the following multiclass classifiers, Decision Trees, Random Forest, Gradient Boosting. The evaluating metric was the F1- Score and accuracy. The target column or predictor column was in categorical data, then we ran the Term-Transform which converted the categorical data to a Key Numeric type