The field of machine learning is undergoing a significant transformation, with a focus on developing more efficient and scalable methods. Recent advancements in data compression, pruning techniques, and novel training methods have shown promising results in reducing computational costs and improving model performance. One of the key areas of research is the development of methods that can reduce the need for large amounts of labeled data and computational resources. Techniques such as data compression and pruning have been shown to significantly accelerate training, reduce memory usage, and cut storage costs, without sacrificing model performance. The field of deep learning is also witnessing a significant shift towards understanding the underlying principles and phenomena that drive its success. Researchers are moving away from ad hoc explanations and instead focusing on developing broad explanatory theories that can be applied to various deep learning models. Noteworthy papers in this area include dreaMLearning, which introduces a novel framework for learning from compressed data, and Partial Forward Blocking, which proposes a novel data pruning paradigm for lossless training acceleration. The development of parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and its variants, has also enabled efficient model adaptation while reducing computational overhead. Furthermore, researchers are exploring alternatives to traditional backpropagation methods, such as forward-mode automatic differentiation and zero-order optimization, to improve the efficiency of model training and deployment. The use of activation checkpointing and progressive precision update has also been shown to improve the efficiency of model training and deployment, enabling the transmission of lower-bit precision models and reducing bandwidth usage and latency. Overall, the advances in machine learning and deep learning have the potential to unlock new possibilities for distributed and federated learning, as well as tinyML on resource-constrained edge devices. As research in this area continues to evolve, we can expect to see significant improvements in the scalability and performance of machine learning models, leading to wider adoption and more innovative applications.