The text explores the challenges of using less popular datasets for machine and deep learning tasks and introduces Activeloop's Hub as a solution. Hub allows for efficient storage and access to datasets as cloud-native multidimensional arrays, simplifying the data wrangling process and enabling seamless operations across different machines. The tutorial showcases how to use Hub to manage the "Hot Dog - Not Hot Dog" dataset, employing the platform's features like version control and simplified dataset loading. It further demonstrates the process of building a binary image classifier using ResNet18, emphasizing data preprocessing and transfer learning. The tutorial highlights the ease of converting datasets into PyTorch-compatible formats and training a model with minimal code compared to traditional methods. Overall, the text underscores the benefits of Data 2.0, where data management is streamlined, allowing machine learning engineers to focus more on model training and less on data handling.