Distributed machine learning is essential for handling large-scale data in machine learning projects, especially when traditional methods fall short due to scalability and efficiency limitations. It addresses these challenges by distributing data and computational processes across multiple worker nodes, enabling parallel processing and speeding up model training. This approach is particularly useful in deep learning projects and applications like healthcare and advertising, where vast amounts of data are involved. There are two main types of distributed machine learning: data parallelism, which involves each node working on a subset of data with a copy of the model, and model parallelism, which segments the model across nodes. Despite its advantages, distributed machine learning faces challenges such as scalability, convergence, and fault tolerance, which can be mitigated by strategies like task parallelization and periodic checkpoints. Successful implementation often requires a robust MLOps platform with specialized integrations, such as Comet's Python SDK, to support distributed training.