The article provides a detailed guide on integrating TensorFlow, an open-source software library for neural network training, with Comet, an online platform for monitoring and logging experiments. Neural networks, designed to model the human brain, consist of interconnected nodes that process information in layers, with deep learning networks having more than three layers to enhance prediction accuracy. TensorFlow operates by passing data through a graph of nodes that perform mathematical operations, while Comet simplifies the evaluation of system metrics and parameters by allowing users to compare various experimental configurations. The integration process involves configuring Comet to automatically log the desired TensorFlow objects, such as graphs and histograms, and includes an example using the Boston Housing dataset to demonstrate building and compiling a TensorFlow model for a regression task. The guide also covers viewing experiment results within Comet, emphasizing the platform's utility in improving visibility, reproducibility, and collaboration in machine learning operations.