- Successfully implemented a computer network intrusion detection project using deep learning

- Compiled and utilized a dataset of 5 million records of network traffic data

- Trained the neural network model using cloud services for improved performance

- Attained an impressive accuracy rate of 98% and enhanced prediction capabilities

- Enabled more accurate intrusion detection by training the models to identify patterns and anomalies in network traffic data

- Leveraged AWS, MLflow, and Bentoml to establish a comprehensive end-to-end machine learning workflow.