The lifecycle of a large language model splits into two fundamentally distinct phases: training and inference. While both involve passing data through neural networks, the computational demands, objectives, constraints, and optimization strategies differ so dramatically that they might as well be separate disciplines. Training is the expensive, time-intensive process of teaching a model to understand … Read more
Training machine learning models is both an art and a science. To achieve high performance and reliability, data scientists and machine learning engineers must follow a set of best practices. In this blog post, we will explore the essential steps and strategies for training machine learning models, ensuring they perform well in real-world scenarios. We … Read more
This article discusses the relationship between batch size and training in machine learning. We will explore the fundamental concepts of batch size and its significance in training. Subsequently, we will learn the effects of different batch sizes on training dynamics, discussing both the advantages and disadvantages of small and large batch sizes. Finally, you will … Read more
