The tutorial provides a comprehensive guide on building an image recognition model using a convolutional neural network (CNN) with MXNet Gluon, integrated with Comet.ml for experiment tracking. It employs the ResNet model architecture, specifically designed to handle the vanishing gradient problem, and applies it to the CIFAR-10 dataset, which includes 60,000 images across 10 classes. The tutorial details the steps for setting up the environment, including hardware and software requirements, and emphasizes the importance of data augmentation and learning rate adjustments to reduce overfitting. The model's performance is monitored using Comet.ml, which allows real-time tracking of training and validation accuracy, and enables comparison between different model iterations. Adjustments such as batch size and learning rate decay are explored to optimize the model, highlighting the challenges of achieving a balance between training and validation accuracy to prevent overfitting. Overall, the tutorial serves as a starting point for using MXNet Gluon and Comet.ml in image classification tasks.