Скачать или смотреть Leveraging Distributed Computing Frameworks

@genaiexp Distributed computing frameworks, such as Apache Spark and Hadoop, play a pivotal role in optimizing data flow within ML pipelines. These frameworks enable the processing of large datasets across clusters of computers, facilitating parallel processing and significantly reducing computation time. Apache Spark, with its in-memory processing capabilities, offers tremendous speed advantages, especially for iterative algorithms common in machine learning. Hadoop, on the other hand, is well-suited for batch processing and handling large-scale data storage. The choice of framework depends on the specific requirements of your ML project. Consider factors like data volume, processing speed, and the nature of the tasks. Leveraging these frameworks allows for the distribution of data and computations, enhancing scalability and performance. By using distributed computing effectively, you can optimize data flow, ensuring your ML pipelines are both efficient and capable of handling large-scale data processing tasks.