Abstract: Deep Neural Networks (DNNs) have witnessed remarkable advancements across diverse domains, continually scaling in size and training on increasingly expansive datasets. This progression has bestowed upon them the remarkable ability to swiftly adapt to novel tasks with minimal training data. However, the journey of training models comprising tens to hundreds of billions of parameters on vast datasets is anything but straightforward. This lecture is designed to offer comprehensive insights into the intricacies of training and deploying the most expansive neural networks.